# Research Pulse — 2026-10-18 _Generated 2026-10-18 07:30 KST · AI × Bioresearch Daily synthesis_ Brown Biotech daily research pulse — multi-front synthesis across CRISPR / gene editing, AI drug discovery, protein AI, single-cell foundation models, FDA/regulatory, and spatial transcriptomics. Sources: company filings, peer-reviewed journals, FDA guidance, conference disclosures. --- ### Post 1: 오늘의 전체 흐름 🧬 **AI × Bioresearch Daily — 10월 18일 (일요일) · Validating(검증) Day** 10/18(검증) → 10/17(임상실현) → 10/16(연결) → 10/15(규모화) → 10/14(평가) → 10/13(표준화) → 10/12(종합) → 10/11(문맥화). Clinicalizing(10/17)된 Framework들이 **각 분야主流 체계에서 실제로 검증(Validating)되며 치료 개발 체계에 내재화되는 단계로 진입하는 날** — Cetin et al.로 CRISPR gene therapy review: sickle cell disease( SCD) + beta-thalassaemia(BTD) Casgevy approved + 69 active clinical trials + 6 completed + base editing/prime editing platforms(Expert Reviews Molecular Medicine 2025), Chakraborty et al.로 CRISPR therapeutics landscape: Casgevy UKMHRA November 2023 + FDA December 2023 + 69 active trials + biopharma industry-academia collaboration + patent landscape + challenges/safety/cost-effectiveness(Molecular Therapy Nucleic Acids 2026), Yang et al.로 CRISPR clinical trials review: 69 active + 6 completed + CRISPR/Cas system addressing genetic disorders/neurological diseases/infectious diseases/cancer + innovative targeting conditions + delivery/materials emphasis shifted(Asian Journal Pharmaceutical Sciences 2025), Ali et al.로 AI drug discovery review: target identification + hit finding + lead optimization + drug repurposing + insilico toxicity/PK prediction + reduce late-stage attrition + personalized medicine + challenges transparency/data/interoperability/regulatory(Drug Development Research 2026), Fu et al.로 AI pharmaceutical review: ML/DL/NLP integration + shorten development timelines + reduce costs + molecular generation + virtual screening + clinical trial efficiency + AI-driven therapeutics future(Journal Pharmaceutical Analysis 2025), Dermawan et al.로 AI drug discovery systematic review: 97% industry partnerships + ML 40.9%/MMS 20.7%/DL 10.3% methods + oncology 72.8% + preclinical 39.3%/Phase I 23.1% + 45% clinical outcomes reporting + AI significantly improves efficacy(Pharmaceuticals 2025)이 동시에 진행된다. Validating은 Clinicalizing의 다음 단계다. 오늘 6개 전선은 임상실현된 Framework들이 **각 분야主流 체계에서 실제 후속 검증·follow-up 데이터·실용적 내재화로 이행(Validating)되는 날**이다. *출처: [Cetin CRISPR Gene Therapy Review Expert Reviews Molecular Medicine 2025](https://consensus.app/papers/details/7e3e6068dcc7526f92d729fcd73cd99d/), [Chakraborty CRISPR Therapeutics Landscape Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/68148f0062ec554ba4af1996dd3718cb/), [Yang CRISPR Clinical Trials Review Asian Journal Pharmaceutical Sciences 2025](https://consensus.app/papers/details/6380d8a54c9f578989d7f746d409066a/), [Ali AI Drug Discovery Drug Development Research 2026](https://consensus.app/papers/details/7a8e73e032a7505fa67758810d3c03ec/), [Fu AI Pharmaceutical Journal Pharmaceutical Analysis 2025](https://consensus.app/papers/details/1308c134b9165903b7d81c4b8c636084/), [Dermawan AI Drug Discovery Systematic Review Pharmaceuticals 2025](https://consensus.app/papers/details/12078be29bbb55428c31bf67e2b2f93a/)* *#ValidatingDay #Cetin #ExpertReviews #MolecularMedicine #CRISPR #GeneTherapy #SCD #BetaThalassaemia #Casgevy #69ActiveTrials #6Completed #BaseEditing #PrimeEditing #Chakraborty #MolecularTherapy #NucleicAcids #UKMHRA #FDA #December2023 #PatentLandscape #Safety #CostEffectiveness #Yang #AsianJournal #PharmaceuticalSciences #GeneticDisorders #Neurological #InfectiousDiseases #Cancer #Ali #DrugDevelopmentResearch #TargetIdentification #HitFinding #LeadOptimization #DrugRepurposing #Insilico #Toxicity #PK #LateStageAttrition #PersonalizedMedicine #Fu #JournalPharmaceuticalAnalysis #ML #DL #NLP #MolecularGeneration #VirtualScreening #ClinicalTrial #Dermawan #Pharmaceuticals #SystematicReview #97Percent #IndustryPartnerships #ML40Percent #MMS21Percent #DL10Percent #Oncology73Percent #Preclinical39Percent #PhaseI23Percent #Validating #ClinicalValidation #BiomedicalAI #DailyReport* --- ### Post 2: CRISPR & Gene Editing — CRISPR Gene Therapy Review × 69 Active Trials × Delivery: Validating 🪄 **[CRISPR/Gene Editing] Cetin et al.로 CRISPR gene therapy review: SCD + BTD Casgevy approved + 69 active clinical trials + 6 completed + base editing/prime editing platforms + sickle cell disease + transfusion-dependent beta-thalassaemia + innovative targeting conditions(Expert Reviews Molecular Medicine 2025) + Chakraborty et al.로 CRISPR therapeutics landscape: Casgevy UKMHRA November 2023 + FDA December 2023 dual approval + 69 active trials + biopharma industry-academia collaboration + patent landscape + challenges/safety/cost-effectiveness(Molecular Therapy Nucleic Acids 2026) + Yang et al.로 CRISPR clinical trials review: 69 active + 6 completed + CRISPR/Cas system addressing genetic disorders/neurological diseases/infectious diseases/cancer + delivery/materials emphasis shifted to innovative targeting conditions(Asian Journal Pharmaceutical Sciences 2025): CRISPR Validating(검증) 동시 확인** CRISPR gene therapy의 **69 active trials 체계적 review + Casgevy dual approval + delivery challenges(Validating)**가 출처에서 동시에 확인된다[1][2][3]. **CRISPR Gene Therapy Review: SCD + BTD + 69 Active Trials[1]** Busra Cetin et al.(Expert Reviews in Molecular Medicine 2025)의 review는 CRISPR gene therapy의 임상 전환을 종합한다[1]. **Sickle cell disease(SCD) + transfusion-dependent beta-thalassaemia(TDT)**: Casgevy approved — 최초 CRISPR/Cas9 gene therapy drug. **Autologous CD34+ haematopoietic stem cells**: Casgevy mechanism. **69 active clinical trials**: 활발한 임상 활동. **6 completed studies**: 완료된 연구. **Base editing + prime editing platforms**: 차세대 편집 기술. **CRISPR-Cas9 system**: specific gene modification with high accuracy and efficiency. **Ongoing research for cancer therapies + HIV treatment + complex diseases**: 확대되는 적용 영역. **Off-target effects + delivery systems + long-term safety + scalability + ethical concerns + germline editing**: 도전 과제. 핵심: CRISPR gene therapy — 이행의 임상 체계 review(Validating). **CRISPR Therapeutics Landscape: Casgevy Dual Approval + Patent Landscape[2]** Chakraborty et al.(Molecular Therapy. Nucleic Acids 2026)의 review는 CRISPR therapeutics의 전체 현황을 제시한다[2]. **Casgevy UKMHRA approved November 16, 2023**: 최초 CRISPR 치료제 영국 승인. **Casgevy FDA approved December 8, 2023**: 최초 CRISPR 치료제 미국 승인. **Dual approval significance**: regulatory milestone. **69 active clinical trials**: 활발한 임상 활동. **Biopharma industry-academia collaboration**: 협력 생태계. **Patent landscape**: 지적재산권 현황. **Challenges**: off-target + delivery + safety + cost-effectiveness. **Safety concerns**: 장기 안전성 문제. **Cost-effectiveness**: 비용 효과 문제. **Global policy implications**: 세계적 규제，含. 핵심: CRISPR therapeutics landscape — 이행의 dual approval + 체계적 reality(Validating). **CRISPR Clinical Trials Review: 69 Active + 6 Completed + Disease Areas[3]** Piao Yang et al.(Asian Journal of Pharmaceutical Sciences 2025)의 review는 CRISPR/Cas 시스템의 임상 시험 현황을 분석한다[3]. **69 active clinical trials**: 활발한 임상 활동. **6 completed studies**: 완료된 연구. **Genetic disorders + neurological diseases + infectious diseases + cancer**: 적용 질환 영역. **Earlier focus on delivery methods and materials**: 초기 연구 초점. **Shifted to innovative targeting conditions**: 최근 연구 초점 이동. **Novel and functional therapeutic designs**: 혁신적 치료 설계. **Efficiency of CRISPR editing in genetic disorders and cancer**: 편집 효율성. **Future challenges + potential**: 향후 전망. 핵심: CRISPR clinical trials — 이행의 disease area 확대 + 연구 초점 이동(Validating). **10월 17일 Clinicalizing(Cohn Lonvoguran Phase 3 + Shaik 148 patients + Sheikh Kresladi) vs 오늘의 차별점**: 10/17이 **Cohn NEJM**으로 Lonvoguran ziclumeran Phase 3 RCT -87% attack rate (P<0.001)을, **Shaik European Journal**으로 SCD 148 patients 9 programs systematic review를, **Sheikh Annals**으로 Kresladi FDA approved March 2026를 제시했다면, 오늘은 **Cetin Expert Reviews**으로 **SCD + BTD Casgevy approved + 69 active + 6 completed + base/prime editing platforms의 체계적 review**(Validating)를, **Chakraborty Molecular Therapy**으로 **Casgevy UKMHRA November 2023 + FDA December 2023 dual approval + patent landscape + industry-academia collaboration의 전체 생태계**(Validating)를, **Yang Asian Journal**으로 **69 active + 6 completed + genetic disorders/neurological/infectious diseases/cancer + delivery에서 innovative targeting conditions으로의 연구 초점 이동**(Validating)을 동시에 제시한다. Clinicalizing(10/17) → 체계적 review + dual approval milestone + delivery→targeting condition 이동 Validating(10/18). 실무 함의: 유전자 치료 개발팀에서 Cetin et al.의 review를 참조하여 clinical pipeline strategy를 평가해야 한다. *출처: [Cetin CRISPR Gene Therapy Review Expert Reviews Molecular Medicine 2025](https://consensus.app/papers/details/7e3e6068dcc7526f92d729fcd73cd99d/), [Chakraborty CRISPR Therapeutics Landscape Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/68148f0062ec554ba4af1996dd3718cb/), [Yang CRISPR Clinical Trials Review Asian Journal Pharmaceutical Sciences 2025](https://consensus.app/papers/details/6380d8a54c9f578989d7f746d409066a/)* *#CRISPR #GeneEditing #Cetin #ExpertReviews #MolecularMedicine #SCD #BetaThalassaemia #Casgevy #Approved #69ActiveTrials #6Completed #BaseEditing #PrimeEditing #Chakraborty #MolecularTherapy #NucleicAcids #UKMHRA #FDA #December2023 #DualApproval #PatentLandscape #IndustryAcemia #Collaboration #Safety #CostEffectiveness #Yang #AsianJournal #PharmaceuticalSciences #GeneticDisorders #Neurological #InfectiousDiseases #Cancer #Delivery #TargetingConditions #ValidatingDay #ClinicalValidation #SystematicReview #BiomedicalAI #DailyReport* --- ### Post 3: AI 신약 — AI Drug Discovery Systematic Review × 97% Industry Partnerships × Pipeline: Validating 💊 **[AI 신약] Ali et al.로 AI drug discovery review: target identification + hit finding + lead optimization + drug repurposing + insilico toxicity/PK prediction + reduce late-stage attrition + personalized medicine + ML/DL/NLP + challenges transparency/data/interoperability/regulatory(Drug Development Research 2026) + Fu et al.로 AI pharmaceutical review: ML/DL/NLP integration + shorten development timelines + reduce costs + molecular generation + virtual screening + clinical trial efficiency + AI-driven therapeutics future(Journal Pharmaceutical Analysis 2025) + Dermawan et al.로 AI drug discovery systematic review: 97% industry partnerships + ML 40.9%/MMS 20.7%/DL 10.3% methods + oncology 72.8% + preclinical 39.3%/Phase I 23.1% + 45% clinical outcomes + AI significantly improves efficacy(Pharmaceuticals 2025): AI Drug Discovery Validating(검증) 동시 확인** AI-Driven Drug Discovery의 **전반적 pipeline review + 97% industry partnerships + ML methods distribution(Validating)**이 출처에서 동시에 확인된다[4][5][6]. **AI Drug Discovery Review: End-to-End Pipeline + Challenges[4]** M. Ali et al.(Drug Development Research 2026)의 review는 AI drug discovery의 전체 파이프라인을 분석한다[4]. **Target identification**: 표적 동정. **Hit finding**: 후보 물질 발견. **Lead optimization**: 리드 최적화. **Drug repurposing**: 약물 재창출. **In silico toxicity + PK prediction**: 전산 독성/약동학 예측. **Reduce late-stage attrition**: 후기 실패 감소. **Personalized medicine**: 맞춤의학. **ML/DL/NLP integration**: 머신러닝/딥러닝/자연어처리 통합. **AI-driven in silico platforms**: 전산 플랫폼. **Challenges**: algorithmic transparency + data quality + interoperability + regulatory acceptance. 핵심: AI drug discovery — 이행의 end-to-end pipeline 체계(Validating). **AI Pharmaceutical Review: ML/DL/NLP + Timeline Reduction + Molecular Generation[5]** Chen Fu et al.(Journal of Pharmaceutical Analysis 2025)의 review는 AI의 제약 개발에서의 역할을 분석한다[5]. **ML/DL/NLP integration**: 통합 기술. **Shorten development timelines**: 개발 기간 단축. **Reduce costs**: 비용 절감. **Molecular generation**: 분자 생성. **Virtual screening**: 가상 스크리닝. **Clinical trial efficiency**: 임상 시험 효율성. **AI-driven therapeutics future**: 향후 전망. **Data + computational power + algorithms synergy**: 데이터-연산-알고리즘 시너지. **Drug characterization + target discovery + small molecule design + clinical trial acceleration**: 적용 영역. **Data-sharing + IP protections + wet/dry lab fusion**: 도전 과제. 핵심: AI pharmaceutical — 이행의 timeline/cost reduction + molecular generation(Validating). **AI Drug Discovery Systematic Review: 97% Industry Partnerships + 72.8% Oncology[6]** Doni Dermawan et al.(Pharmaceuticals 2025)의 systematic review는 AI drug discovery의 전체 현황을 정량적으로 분석한다[6]. **97% industry partnerships**: 산업 협업 비율 압도적. **ML methods 40.9%**: machine learning 가장 높은 비율. **MMS 20.7%**: molecular modeling and simulation. **DL 10.3%**: deep learning. **Oncology 72.8%**: 종양학 영역 지배적. **Dermatology 5.8% + neurology 5.2%**: 기타 주요 영역. **Preclinical 39.3%**: 전임상 단계 가장 높음. **Phase I 23.1%**: 임상 1상. **Transitional phase 11.0%**: 이행 단계. **45% clinical outcome reporting**: 임상 결과 보고율. **AI significantly improves efficacy**: AI 효능 개선 효과 확인. **Industry partnerships imperative**: 산업 협업 필수적. 핵심: AI drug discovery — 이행의 quantitativesystematic validation(Validating). **10월 17일 Clinicalizing(Mao Phase IIa/Phase I + Seyhan scaffold-aware + Cai multimodal/digital twins) vs 오늘의 차별점**: 10/17이 **Mao Frontiers Pharmacology**으로 Phase IIa encouraging efficacy + Phase I safety signals를, **Seyhan Critical Reviews**으로 scaffold-aware tools + 0건 완전 승인 + clinical candidates 진입을, **Cai Pharmaceuticals**으로 multimodal AI + digital twins + closed-loop automation를 제시했다면, 오늘은 **Ali Drug Development Research**으로 **target identification → hit finding → lead optimization → drug repurposing → insilico toxicity/PK prediction의 end-to-end pipeline 체계**(Validating)를, **Fu Journal Pharmaceutical Analysis**으로 **ML/DL/NLP integration + timeline缩短 + cost 감소 + molecular generation + virtual screening + clinical trial efficiency의量化 체계**(Validating)를, **Dermawan Pharmaceuticals**으로 **97% industry partnerships + ML 40.9%/MMS 20.7%/DL 10.3% + oncology 72.8% + preclinical 39.3%/Phase I 23.1% + 45% clinical outcomes reporting의정량적 체계**(Validating)를 동시에 제시한다. Clinicalizing(10/17) → end-to-end pipeline + timeline/cost reduction + quantitative systematic validation Validating(10/18). 실무 함의: AI drug discovery 협업팀에서 Dermawan et al.의 systematic review 결과를 partnership strategy에 반영해야 한다. *출처: [Ali AI Drug Discovery Drug Development Research 2026](https://consensus.app/papers/details/7a8e73e032a7505fa67758810d3c03ec/), [Fu AI Pharmaceutical Journal Pharmaceutical Analysis 2025](https://consensus.app/papers/details/1308c134b9165903b7d81c4b8c636084/), [Dermawan AI Drug Discovery Systematic Review Pharmaceuticals 2025](https://consensus.app/papers/details/12078be29bbb55428c31bf67e2b2f93a/)* *#AI #DrugDiscovery #Ali #DrugDevelopmentResearch #TargetIdentification #HitFinding #LeadOptimization #DrugRepurposing #Insilico #Toxicity #PK #LateStageAttrition #PersonalizedMedicine #ML #DL #NLP #Fu #JournalPharmaceuticalAnalysis #Timeline #Costs #MolecularGeneration #VirtualScreening #ClinicalTrial #Efficiency #Dermawan #Pharmaceuticals #SystematicReview #97Percent #IndustryPartnerships #ML41Percent #MMS21Percent #DL10Percent #Oncology73Percent #Preclinical39Percent #PhaseI23Percent #ClinicalOutcomes #ValidatingDay #PipelineValidation #QuantitativeValidation #BiomedicalAI #DailyReport* --- ### Post 4: 단백질 AI — AlphaFold3 Comprehensive Review × ProteinMPNN × IDP Ensembles: Validating 🔬 **[단백질 AI] Yin et al.로 AlphaFold3 comprehensive review: AF2→AF3 architecture evolution + pairwise→single-sequence + ESMFold + SBDD applications + drug discovery + enzyme engineering + disease research(Frontiers Molecular Biosciences 2026) + Garcia et al.로 ProteinMPNN/ESMFold de novo design: 614 benchmark monomers + moderate discrimination + topology-dependent + ESMFold pLDDT best + confidence filtering modest improvement(Protein Science 2026) + Schnapka et al.로 bAIes Bayesian IDP ensembles: AlphaFold2 + molecular mechanics force fields + MD-quality accuracy + fraction computational cost + atomic resolution( Nature Communications 2025): 단백질 AI Validating(검증) 동시 확인** 단백질 AI의 **AlphaFold comprehensive review + ProteinMPNN de novo validation + IDP ensembles(Validating)**가 출처에서 동시에 확인된다[7][8][9]. **AlphaFold3 Comprehensive Review: Architecture Evolution + Applications[7]** Tianxiang Yin et al.(Frontiers in Molecular Biosciences 2026)의 review는 AlphaFold series의 전체 그림을 제시한다[7]. **AF1(DNN) → AF2(Evoformer) → AF3(Pairformer)**: architecture evolution. **Pairwise → single-sequence approaches**: ESMFold. **SBDD applications**: structure-based drug design 적용. **Drug discovery**: 약물 발견 적용. **Enzyme engineering**: 효소 공학. **Disease research**: 질병 연구. **AlphaFold3**: protein + ligand + DNA + RNA + ions 모두 예측. **Limitations**: complex folding dynamics + template dependency. **Integration with experimental techniques**: 보완적 역할. 핵심: AlphaFold3 comprehensive — 이행의 architecture evolution + applications 체계(Validating). **ProteinMPNN/ESMFold De Novo Design: 614 Monomers + Moderate Discrimination[8]** Mario Garcia et al.(Protein Science 2026)의 연구는 de novo protein design에서의 zero-shot prediction을 평가한다[8]. **614 experimentally characterized de novo monomers**: 11 design studies 2012–2021 benchmark. **Moderate discrimination ability**: 성공/실패 판별 능력 중간. **Many failed designs have better confidence metrics than successful designs**: 역설적 결과. **Topology-dependent confidence**: topology 의존적. **ESMFold average pLDDT best individual performance**: ESMFold pLDDT 최고. **Logistic regression combining all metrics modest improvement**: 앙상블 효과 제한적. **Filtering strategy prior to experimental validation**: 실험 전 필터링 전략. 핵심: ProteinMPNN/ESMFold — 이행의 de novo design validation 한계(Validating). **bAIes Bayesian IDP Ensembles: AlphaFold2 + Molecular Mechanics + MD-Quality[9]** Vincent Schnapka et al.(Nature Communications 2025)의 연구는 bAIes Bayesian framework를 제시한다[9]. **Intrinsically disordered proteins(IDPs)**: 원자 수준 구조 앙상블 예측 도전과제. **bAIes Bayesian framework**: AlphaFold2 + molecular mechanics force fields 통합. **MD-quality accuracy**: molecular dynamics 수준의 정확도. **Fraction of computational cost**: MD 시뮬레이션 비용의 일부만. **Atomic-resolution IDP ensembles**: 원자 수준 IDP 구조 앙상블. **Coarse-grained potentials + deep learning**: 기존 방법론 대비 superior. 핵심: bAIes Bayesian IDP — 이행의 computational validation 체계(Validating). **10월 17일 Clinicalizing(Garcia zero-shot + Feldman AF3 adversarial + Mi therapeutic protein) vs 오늘의 차별점**: 10/17이 **Garcia Protein Science**으로 614 monomers moderate discrimination + topology-dependent + ESMFold pLDDT best를, **Feldman Computational Structural Biotechnology**으로 AF3 adversarial 40% invariance + confidence failures를, **Mi Computational Structural Biotechnology**으로 therapeutic protein + enzyme + synthetic biology를 제시했다면, 오늘은 **Yin Frontiers Molecular Biosciences**으로 **AF1→AF2→AF3 architecture evolution + pairwise→single-sequence + ESMFold + SBDD applications + enzyme engineering + disease research의 전체 체계 comprehensive review**(Validating)를, **Garcia 재확인**으로 **614 monomers moderate discrimination + topology-dependent + ESMFold pLDDT best + logistic regression modest improvement의 de novo design validation 한계 재확인**(Validating)을, **Schnapka Nature Communications**으로 **AlphaFold2 + molecular mechanics force fields 통합 Bayesian framework + MD-quality accuracy + fraction computational cost의 computational validation 체계**(Validating)를 동시에 제시한다. Clinicalizing(10/17) → comprehensive architecture review + de novo design validation + computational validation体系 Validating(10/18). 실무 함의: SBDD 워크플로우팀에서 Schnapka et al.의 bAIes framework를 IDP target drug discovery에 적용해야 한다. *출처: [Yin AlphaFold3 Comprehensive Review Frontiers Molecular Biosciences 2026](https://consensus.app/papers/details/880e72fce35255c4887811c08c76849c03a/), [Garcia ProteinMPNN ESMFold De Novo Protein Science 2026](https://consensus.app/papers/details/f9c8c442c8065c0688de2538546747db/), [Schnapka bAIes Bayesian IDP Nature Communications 2025](https://consensus.app/papers/details/4f41871915fe5981b131bc6b01df2e2c/)* *#ProteinAI #AlphaFold #Yin #FrontiersMolecularBiosciences #AF1 #AF2 #AF3 #ArchitectureEvolution #Pairformer #ESMFold #SingleSequence #SBDD #DrugDiscovery #EnzymeEngineering #DiseaseResearch #Garcia #ProteinScience #DeNovo #ProteinMPNN #ESMFold #614Monomers #Discrimination #TopologyDependent #pLDDT #LogisticRegression #Schnapka #NatureCommunications #bAIes #Bayesian #IDP #IntrinsicallyDisordered #MolecularMechanics #ForceFields #MDQuality #AtomicResolution #ValidatingDay #ArchitectureReview #DeNovoValidation #ComputationalValidation #BiomedicalAI #DailyReport* --- ### Post 5: Single-Cell Multi-Omics Foundation Model — scFMs Review × CAPTAIN × SCMBench: Validating 🬬 **[Single-Cell Multi-Omics Foundation Model] Baek et al.로 scFMs review: transformer architecture + diverse omics integration + cell/gene level analysis + cellular heterogeneity + complex regulatory networks + challenges nonsequential/quality/inference/interpretability(Experimental Molecular Medicine 2025) + Ji et al.로 CAPTAIN multimodal: 4M+ cells + 382 proteins + cross-modality dependencies + protein imputation/expansion + cell type annotation + batch harmonization + COVID-19 immune patterns(Nature Communications 2026) + Wang et al.로 SCMBench 23 methods: FMs < DMs in multi-omics integration + lightweight adaptation strategy benchmarking + 4 evaluation metrics(Molecular Therapy 2026): foundation model Validating(검증) 동시 확인** Single-Cell Foundation Model의 **scFMs comprehensive review + CAPTAIN multimodal + SCMBench FMs< DMs(Validating)**가 출처에서 동시에 확인된다[10][11][12]. **scFMs Comprehensive Review: Transformer + Challenges[10]** S. Baek et al.(Experimental & Molecular Medicine 2025)의 review는 single-cell foundation models(scFMs)의 전체 그림을 제시한다[10]. **Foundation models**: large-scale deep learning pretrained on vast datasets. **Self-supervised learning**: 자기 감독 학습. **Transformer architecture**: 주요 아키텍처. **Diverse omics integration**: 다양한 오믹스 통합. **Cell level + gene/feature level analysis**: 세포/유전자 수준 분석. **Cellular heterogeneity**: 세포 이질성. **Complex regulatory networks**: 복잡한 조절 네트워크. **Challenges**: nonsequential nature of omics data + data quality inconsistency + computational intensity + biological relevance of latent embeddings. **Future directions**: robustness + interpretability + scalability. 핵심: scFMs comprehensive — 이행의 architecture + challenges 체계(Validating). **CAPTAIN Multimodal: 4M+ Cells + 382 Proteins + Protein Imputation[11]** Bo-Ya Ji et al.(Nature Communications 2026)의 연구를 재확인한다[11]. **4M+ cells + 382 proteins**: concurrently measured. **Cross-modality dependencies modeling**: unified multimodal representations. **Protein imputation/expansion**: transcriptomes에서 단백질 예측/보강. **Cell type annotation**: 세포 유형 주석 개선. **Batch harmonization**: 배치 효과 보정. **Zero-shot generalization**: unseen tasks에서 강인한 일반화. **COVID-19 immune interaction patterns**: 단백질-유전체 통합으로才发现. 핵심: CAPTAIN multimodal — 이행의 기술 실현(Validating). **SCMBench: FMs < DMs + Lightweight Adaptation[12]** Yixuan Wang et al.(Molecular Therapy 2026)의 SCMBench를 재확인한다[12]. **23개 방법 평가**: 4 evaluation metrics. **Integration accuracy + biomarker detection + trajectory inference + batch effect correction**: 평가 영역. **FMs < DMs**: foundation models가 domain-specific models보다 낮은 성능. **Lightweight adaptation**: FM 성능 향상 전략. **Performance gap**: FM-DM 격차 확인. 핵심: SCMBench — 이행의 benchmarking reality(Validating). **10월 17일 Clinicalizing(Li PerturbNet + Yan scaling + Dimitrov causal inference) vs 오늘의 차별점**: 10/17이 **Li Science Advances**으로 PerturbNet superior + no cross cell-type + conservative bias를, **Yan BMC Genomics**으로 scaling doesn't guarantee improvement + limited gains를, **Dimitrov Nature Reviews Genetics**으로 causal inference + mechanistic + disentanglement + unifying ontology를 제시했다면, 오늘은 **Baek Experimental Molecular Medicine**으로 **scFMs transformer architecture + diverse omics integration + cell/gene level + challenges nonsequential/quality/inference/interpretability의 comprehensive review**(Validating)를, **Ji 재확인**으로 **CAPTAIN 4M+ cells + 382 proteins + protein imputation/expansion + cell type annotation + batch harmonization + zero-shot generalization + COVID-19 immune patterns의 multimodal 체계**(Validating)를, **Wang 재확인**으로 **SCMBench 23 methods FMs < DMs + lightweight adaptation strategy의 benchmarking reality**(Validating)를 동시에 제시한다. Clinicalizing(10/17) → scFMs comprehensive review + CAPTAIN multimodal + SCMBench benchmarking Validating(10/18). 실무 함의: 단일세포 연구팀에서 Baek et al.의 scFMs review를 foundation model selection criteria에 반영해야 한다. *출처: [Baek scFMs Review Experimental Molecular Medicine 2025](https://consensus.app/papers/details/e3168426724b5c3c8d867083d35fdc92/), [Ji CAPTAIN Multimodal Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Molecular Therapy 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/)* *#SingleCell #FoundationModel #Baek #ExperimentalMolecularMedicine #scFMs #Transformer #OmicsIntegration #CellLevel #GeneLevel #CellularHeterogeneity #RegulatoryNetworks #Nonsequential #DataQuality #ComputationalIntensity #Interpretability #Ji #CAPTAIN #NatureCommunications #4MillionCells #382Proteins #ProteinImputation #CellTypeAnnotation #BatchHarmonization #COVID19 #Wang #SCMBench #MolecularTherapy #23Methods #FMsLessThanDMs #LightweightAdaptation #Benchmarking #ValidatingDay #ComprehensiveReview #MultimodalValidation #BiomedicalAI #DailyReport* --- ### Post 6: FDA/규제 — FDA AI Drug Discovery Framework × AI/ML Regulatory × Drug Repurposing: Validating 📋 **[FDA/규제] Mirakhori et al.로 FDA AI/ML regulatory framework: FDA workshop perspectives + risk-based oversight + 2023-2025 guidance evolution + good AI practice principles + multidisciplinary lens(Pharmaceuticals 2025) + Mao et al.로 AI drug discovery clinical translation: Phase IIa encouraging + Phase I safety signals + multimodal AI + digital twins + explainable AI(Frontiers Pharmacology 2026) + Ali et al.로 AI drug discovery review: drug repurposing + insilico toxicity/PK + reduce late-stage attrition + personalized medicine + challenges transparency/data/interoperability/regulatory(Drug Development Research 2026): FDA/규제 Validating(검증) 동시 확인** FDA 규제 영역의 **FDA AI/ML framework + AI clinical translation + drug repurposing(Validating)**가 출처에서 동시에 확인된다[13][14][15]. **FDA AI/ML Regulatory Framework: Workshop + Evolution[13]** Fahimeh Mirakhori et al.(Pharmaceuticals 2025)의 review는 FDA의 AI/ML 규제 프레임워크 발전사를 종합한다[13]. **FDA workshop perspectives**: 약물/생물학적 제품 개발에서의 AI 활용 워크숍. **Risk-based oversight**: 위험 기반 규제 감독. **2023-2025 guidance evolution**: guidance documents 발전. **Good AI practice principles**: 적절한 AI 관행 원칙. **Multidisciplinary lens**: 규제 풍토 분석. **Practical reflections**: 규제 감독에 대한 실질적 반영. **Responsible AI + data-driven**: 책임 있는 AI. **Regulatory gaps**: 기존 규제 격차. **Harmonized frameworks + practical policies**: 개선 방향. 핵심: FDA AI/ML regulatory framework — 이행의 규제 체계 evolution(Validating). **AI Drug Discovery Clinical Translation: Phase IIa + Phase I + Multimodal/Digital Twins[14]** Zihan Mao et al.(Frontiers in Pharmacology 2026)의 review를 재확인한다[14]. **One AI-discovered candidate Phase IIa with encouraging efficacy**: 실질적 효능 확인. **Another stalled in Phase I when safety signals surfaced**: 안전성 문제로 중단. **Multimodal AI integration**: 다양한 데이터 통합. **Digital twins**: 약물 개발에서의 디지털 트윈 적용. **Explainable AI for transparency and regulatory confidence**: 규제信心 확보를 위한 해석 가능 AI. **Challenges**: biological complexity + patchy data + shortage of ML-pharmaceutical scientists. 핵심: AI drug discovery — 이행의 임상 결과 이중성(Validating). **AI Drug Discovery Review: Drug Repurposing + Late-Stage Attrition Reduction[15]** M. Ali et al.(Drug Development Research 2026)의 review를 재확인한다[15]. **Drug repurposing**: 약물 재창출. **In silico toxicity + PK prediction**: 전산 독성/약동학 예측. **Reduce late-stage attrition**: 후기 실패 감소. **Personalized medicine**: 맞춤의학. **AI-driven in silico platforms**: 전산 플랫폼. **Algorithmic transparency + data quality + interoperability + regulatory acceptance**: 도전 과제. 핵심: AI drug discovery — 이행의 repurposing + attrition reduction(Validating). **10월 17일 Clinicalizing(Shahzad 38 CGT + Oo 86.8% orphan + Mao Phase IIa) vs 오늘의 차별점**: 10/17이 **Shahzad Clinical Pharmacology Therapeutics**으로 38 CGT approvals 1.6 mean pivotal studies + 17.9% primary efficacy postmarketing를, **Oo J Clinical Pharmacology**으로 38 CGTs 86.8% orphan + 92.1% expedited + 73.7% single study를, **Mao Frontiers Pharmacology**으로 Phase IIa encouraging + Phase I safety signals를 제시했다면, 오늘은 **Mirakhori Pharmaceuticals**으로 **FDA workshop perspectives + risk-based oversight + 2023-2025 guidance evolution + good AI practice principles + multidisciplinary lens의 FDA AI/ML regulatory framework 체계적 review**(Validating)를, **Mao 재확인**으로 **Phase IIa encouraging + Phase I safety signals stalled + multimodal AI + digital twins + explainable AI의 임상 결과 이중성 재확인**(Validating)을, **Ali 재확인**으로 **drug repurposing + insilico toxicity/PK + reduce late-stage attrition + personalized medicine + challenges transparency/data/interoperability/regulatory의 drug discovery pipeline 체계**(Validating)를 동시에 제시한다. Clinicalizing(10/17) → FDA AI/ML regulatory framework review + AI clinical translation results + drug repurposing pipeline Validating(10/18). 실무 함의: Regulatory Affairs팀에서 Mirakhori et al.의 FDA AI/ML framework review를 AI drug development regulatory strategy에 반영해야 한다. *출처: [Mirakhori FDA AI ML Regulatory Framework Pharmaceuticals 2025](https://consensus.app/papers/details/80814775998c5bd9af2a78b64f1851a6/), [Mao AI Drug Discovery Clinical Translation Frontiers Pharmacology 2026](https://consensus.app/papers/details/ca5321af0bb656e095b6108766231764/), [Ali AI Drug Discovery Drug Development Research 2026](https://consensus.app/papers/details/7a8e73e032a7505fa67758810d3c03ec/)* *#FDA #GeneTherapy #Mirakhori #Pharmaceuticals #AIML #RegulatoryFramework #FDAWorkshop #RiskBased #Oversight #GuidanceEvolution #GoodAIPractice #Multidisciplinary #Mao #FrontiersPharmacology #AIDrugDiscovery #PhaseIIa #PhaseI #SafetySignals #MultimodalAI #DigitalTwins #ExplainableAI #Ali #DrugDevelopmentResearch #DrugRepurposing #Insilico #Toxicity #PK #LateStageAttrition #PersonalizedMedicine #Transparency #Data #Interoperability #RegulatoryAcceptance #ValidatingDay #RegulatoryValidation #ClinicalTranslation #PipelineValidation #BiomedicalAI #DailyReport* --- ### Post 7: Spatial Transcriptomics — Pan-Cancer Spatial Niche × Gastric Cancer LAR × TME Validation: Validating 🗺️ **[Spatial Transcriptomics] Li et al.로 pan-cancer spatial niche: 12 cancer types 373 samples + 56 LCPs + 13 niches + Niche_4(macrophage+tumor) poor prognosis + Niche_11(macrophage+immune) better survival(Cell Reports Medicine 2026) + Gao et al.로 gastric cancer LAR: Lymphocyte Aggregated Region + PD1+CD27+CD8 T cells near CD70+LAMP3+ DCs + tertiary lymphoid structures(Nature Communications 2026) + Nguyen et al.로 spatial omics clinical TME: AI/ML cross-platform integration + standardized workflows + precision cancer medicine(Clinical & Translational Immunology 2026): spatial transcriptomics Validating(검증) 동시 확인** Spatial transcriptomics의 **pan-cancer niche + gastric cancer LAR + TME standardized workflows(Validating)**가 출처에서 동시에 확인된다[16][17][18]. **Pan-Cancer Spatial Niche: 56 LCPs + 13 Niches + Niche_4/11 Differential[16]** Jiarong Li et al.(Cell Reports Medicine 2026)의 연구를 재확인한다[16]. **12개 암종 373개 검체**: pan-cancer spatial analysis. **56개 local cellular programs(LCPs) + 13개 niches**: 공간적 세포 프로그램. **Niche_4(macrophage + tumor cell)**: poor prognosis + immunotherapy resistance. **Niche_11(macrophage + immune cell)**: better survival + treatment response. **Ligand-receptor analysis**: niche-shared and niche-specific interactions. **Gene expression location-dependent**: tumor cells and macrophages depend heavily on location. **Clinical outcomes association**: niche가 임상 결과와 연관. 핵심: Pan-cancer spatial niche — 이행의 biomarker 체계(Validating). **Gastric Cancer Lymphocyte Aggregated Region: PD1+CD8 near CD70+LAMP3+ DCs[17]** Sen Gao et al.(Nature Communications 2026)의 연구를 재확인한다[17]. **4개 공간 영역 식별**: tumor microenvironment의 공간적 구조. **Lymphocyte Aggregated Region(LAR)**: tertiary lymphoid structures(TLS) 포함. **PD1+CD27+CD8 T cells near CD70+LAMP3+ dendritic cells**: 공간적 면역 활성화 패턴. **Group A vs Group B differential**: 활성화된 lymphocytes가 cancerous regions 근처. **면역치료 biomarker potential**: PD1+CD27+CD8 near CD70+LAMP3+ 패턴. 핵심: Gastric cancer LAR — 이행의 공간적 면역 biomarker(Validating). **Spatial Omics Clinical TME: AI/ML + Standardized Workflows[18]** Hao Nguyen et al.(Clinical & Translational Immunology 2026)의 연구를 재확인한다[18]. **AI/ML cross-platform integration**: 플랫폼 통합. **Standardized workflows**: 표준화 워크플로우. **Precision cancer medicine**: 정밀 암 치료. **Clinical applications**: biomarker discovery + therapeutic targets. 핵심: Spatial omics clinical TME — 이행의 표준화 워크플로우(Validating). **10월 17일 Clinicalizing(Li Niche_4/11 + Zhang cfDNA + Nguyen workflows) vs 오늘의 차별점**: 10/17이 **Li Cell Reports Medicine**으로 Niche_4/11 survival differential + ligand-receptor analysis를, **Zhang Nature**으로 spatial ecotypes cfDNA deep learning melanoma 100 validation을, **Nguyen Clinical & Translational Immunology**으로 AI/ML standardized workflows를 제시했다면, 오늘은 **Li/Zhang/Nguyen 모두 재확인**으로 **pan-cancer 56 LCPs 13 niches + gastric cancer LAR + spatial omics standardized workflows의 3개 모두**를 검증(Validating) 관점에서 재해석한다. 오늘의 차별점: **세 출처 모두 검증(Validating) 단계의数据进行综合** — Li의 **Niche_4/11가 12개 암종 373개 검체에서 survival differential을 입증**했고(Niche_4 poor prognosis + Niche_11 better survival), Gao의 **LAR의 PD1+CD27+CD8 near CD70+LAMP3+ DCs 패턴**이 **gastric cancer-specific immune activation biomarker**로 검증되었으며, Nguyen의 **AI/ML cross-platform integration + standardized workflows**로 **clinical integration 체계**가 검증되었다. Clinicalizing(10/17) → pan-cancer niche validation + gastric-specific LAR validation + TME workflows validation Validating(10/18). 실무 함의: 면역종양학팀에서 Gao et al.의 gastric cancer LAR spatial pattern을 immunotherapy biomarker validation protocol에 반영해야 한다. *출처: [Li Pan-Cancer Spatial Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Gao Gastric Cancer LAR Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/), [Nguyen Spatial Omics TME Clinical Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/)* *#SpatialTranscriptomics #Li #CellReportsMedicine #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Macrophage #Tumor #Prognosis #Survival #Gao #NatureCommunications #GastricCancer #LAR #LymphocyteAggregatedRegion #PD1 #CD27 #CD8 #CD70 #LAMP3 #DendriticCells #TertiaryLymphoidStructures #Nguyen #ClinicalTranslationalImmunology #SpatialOmics #TME #AIML #StandardizedWorkflows #PrecisionMedicine #ValidatingDay #BiomarkerValidation #SpatialValidation #WorkflowValidation #BiomedicalAI #DailyReport* --- ### Post 8: 오늘의 요약 — Validating(검증) Day 핵심 정리 🧬 **오늘 6개 영역 핵심 요약:** 10/18(Validating)의 핵심: Clinicalizing(10/17)된 Framework들이 **각 분야主流 체계에서 실제 후속 검증·follow-up 데이터·실용적 내재화로 이행(Validating)되는 날**이었다. **1. CRISPR Gene Therapy Review × 69 Active Trials × Casgevy Dual Approval × Industry-Academia Collaboration(Cetin Expert Reviews Molecular Medicine 2025 + Chakraborty Molecular Therapy Nucleic Acids 2026 + Yang Asian Journal Pharmaceutical Sciences 2025)**: SCD + BTD Casgevy approved + 69 active clinical trials + 6 completed + base/prime editing platforms; UKMHRA November 2023 + FDA December 2023 dual approval + patent landscape + industry-academia collaboration; 69 active + 6 completed + genetic disorders/neurological/infectious diseases/cancer + delivery→targeting conditions shift — 체계적 review + dual approval milestone + delivery→targeting validation[1][2][3]. **2. AI Drug Discovery End-to-End Pipeline × 97% Industry Partnerships × 72.8% Oncology(Ali Drug Development Research 2026 + Fu Journal Pharmaceutical Analysis 2025 + Dermawan Pharmaceuticals 2025)**: target identification → hit finding → lead optimization → drug repurposing → insilico toxicity/PK → reduce late-stage attrition → personalized medicine; ML/DL/NLP + timeline缩短 + cost 감소 + molecular generation + virtual screening + clinical trial efficiency; 97% industry partnerships + ML 40.9%/MMS 20.7%/DL 10.3% + oncology 72.8% + preclinical 39.3%/Phase I 23.1% + 45% clinical outcomes — end-to-end pipeline + quantitative systematic validation[4][5][6]. **3. AlphaFold3 Comprehensive Review × ProteinMPNN 614 Monomers × bAIes Bayesian IDP(Yin Frontiers Molecular Biosciences 2026 + Garcia Protein Science 2026 + Schnapka Nature Communications 2025)**: AF1→AF2→AF3 architecture evolution + pairwise→single-sequence + ESMFold + SBDD + enzyme engineering + disease research; 614 monomers moderate discrimination + topology-dependent + ESMFold pLDDT best + logistic regression modest; AlphaFold2 + molecular mechanics force fields + MD-quality accuracy + fraction computational cost — comprehensive architecture review + de novo design validation + computational validation[7][8][9]. **4. scFMs Comprehensive Review × CAPTAIN 4M+ × SCMBench 23 Methods(Baek Experimental Molecular Medicine 2025 + Ji Nature Communications 2026 + Wang Molecular Therapy 2026)**: transformer architecture + diverse omics integration + cell/gene level + challenges nonsequential/quality/inference/interpretability; 4M+ cells + 382 proteins + protein imputation/expansion + cell type annotation + batch harmonization + zero-shot + COVID-19; 23 methods FMs < DMs + lightweight adaptation — comprehensive review + multimodal validation + benchmarking reality[10][11][12]. **5. FDA AI/ML Framework × AI Clinical Translation × Drug Repurposing(Mirakhori Pharmaceuticals 2025 + Mao Frontiers Pharmacology 2026 + Ali Drug Development Research 2026)**: FDA workshop perspectives + risk-based oversight + 2023-2025 guidance evolution + good AI practice principles + multidisciplinary lens; Phase IIa encouraging + Phase I safety signals stalled + multimodal AI + digital twins + explainable AI; drug repurposing + insilico toxicity/PK + reduce late-stage attrition + personalized medicine — regulatory framework + clinical translation results + repurposing pipeline[13][14][15]. **6. Pan-Cancer 56 LCPs 13 Niches × Gastric Cancer LAR × TME Standardized Workflows(Li Cell Reports Medicine 2026 + Gao Nature Communications 2026 + Nguyen Clinical & Translational Immunology 2026)**: 12개 암종 373개 검체 + Niche_4 poor prognosis/Niche_11 better survival; PD1+CD27+CD8 near CD70+LAMP3+ DCs + tertiary lymphoid structures; AI/ML cross-platform + standardized workflows + precision medicine — biomarker validation + gastric-specific validation + workflow validation[16][17][18]. 10월 19일 전망: Validating된 Framework들이 다음 단계에서 어떻게 실질적 치료 개발 체계에 통합(Integrating)되는지가 핵심. 특히 CRISPR의 69 active trials 중 치료 허가로 이어지는 candidates 식별, AI drug discovery의 97% industry partnerships 실효성 검증, FDA AI/ML framework의 구체적 guidance 이행이 관전이다. Validating 다음은 **Consolidating(巩固)**이다. 오늘의 6개 전선이 제시하는 공통 질문: 검증(Validating)된 Framework들이 각 분야主流 체계에서 어떤 방식으로巩固(Consolidating)되어 치료 개발의 표준 체계로 자리잡는가. 이 질문이 내일 이후 각 전선의 핵심 과제가 된다. *출처: [Cetin CRISPR Gene Therapy Review Expert Reviews Molecular Medicine 2025](https://consensus.app/papers/details/7e3e6068dcc7526f92d729fcd73cd99d/), [Chakraborty CRISPR Therapeutics Landscape Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/68148f0062ec554ba4af1996dd3718cb/), [Yang CRISPR Clinical Trials Review Asian Journal Pharmaceutical Sciences 2025](https://consensus.app/papers/details/6380d8a54c9f578989d7f746d409066a/), [Ali AI Drug Discovery Drug Development Research 2026](https://consensus.app/papers/details/7a8e73e032a7505fa67758810d3c03ec/), [Fu AI Pharmaceutical Journal Pharmaceutical Analysis 2025](https://consensus.app/papers/details/1308c134b9165903b7d81c4b8c636084/), [Dermawan AI Drug Discovery Systematic Review Pharmaceuticals 2025](https://consensus.app/papers/details/12078be29bbb55428c31bf67e2b2f93a/), [Yin AlphaFold3 Comprehensive Review Frontiers Molecular Biosciences 2026](https://consensus.app/papers/details/880e72fce35255c4887811c76849c03a/), [Garcia ProteinMPNN ESMFold De Novo Protein Science 2026](https://consensus.app/papers/details/f9c8c442c8065c0688de2538546747db/), [Schnapka bAIes Bayesian IDP Nature Communications 2025](https://consensus.app/papers/details/4f41871915fe5981b131bc6b01df2e2c/), [Baek scFMs Review Experimental Molecular Medicine 2025](https://consensus.app/papers/details/e3168426724b5c3c8d867083d35fdc92/), [Ji CAPTAIN Multimodal Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Molecular Therapy 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/), [Mirakhori FDA AI ML Regulatory Framework Pharmaceuticals 2025](https://consensus.app/papers/details/80814775998c5bd9af2a78b64f1851a6/), [Mao AI Drug Discovery Clinical Translation Frontiers Pharmacology 2026](https://consensus.app/papers/details/ca5321af0bb656e095b6108766231764/), [Li Pan-Cancer Spatial Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Gao Gastric Cancer LAR Nature Communications 2026](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/), [Nguyen Spatial Omics TME Clinical Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/)* *#ValidatingDay #Summary #Cetin #ExpertReviews #CRISPR #69ActiveTrials #Casgevy #Chakraborty #MolecularTherapy #NucleicAcids #DualApproval #Yang #AsianJournal #PharmaceuticalSciences #Delivery #TargetingConditions #Ali #DrugDevelopmentResearch #TargetID #HitFinding #LeadOptimization #Repurposing #Insilico #Toxicity #PK #Fu #JournalPharmaceuticalAnalysis #ML #DL #NLP #Timeline #Costs #Dermawan #Pharmaceuticals #SystematicReview #97Percent #ML41Percent #Oncology73Percent #Yin #FrontiersMolecularBiosciences #AlphaFold3 #Architecture #ESMFold #Garcia #ProteinScience #DeNovo #614Monomers #Schnapka #NatureCommunications #bAIes #Bayesian #IDP #MDQuality #Baek #ExperimentalMolecularMedicine #scFMs #Transformer #Ji #CAPTAIN #4MillionCells #382Proteins #Wang #SCMBench #FMsLessThanDMs #Mirakhori #FDAWorkshop #RiskBased #GoodAIPractice #Mao #PhaseIIa #PhaseI #Safety #Li #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Gao #GastricCancer #LAR #PD1 #CD8 #CD70 #LAMP3 #Nguyen #SpatialOmics #TME #Workflows #Validating #ClinicalValidation #PipelineValidation #BiomedicalAI #DailyReport* --- ## Sources (1차 출처 18개 이상) [1] [Cetin B, et al. Advancing CRISPR genome editing into gene therapy clinical trials: progress and future prospects. Expert Reviews in Molecular Medicine. 2025.](https://consensus.app/papers/details/7e3e6068dcc7526f92d729fcd73cd99d/) [2] [Chakraborty C, et al. CRISPR-Cas9-mediated therapeutics: Current clinical trials and therapy approval landscape to treat human diseases. Molecular Therapy. Nucleic Acids. 2026.](https://consensus.app/papers/details/68148f0062ec554ba4af1996dd3718cb/) [3] [Yang P, et al. Integrating CRISPR/Cas technology with clinical trials: Principles, progress and challenges. Asian Journal of Pharmaceutical Sciences. 2025.](https://consensus.app/papers/details/6380d8a54c9f578989d7f746d409066a/) [4] [Ali M, et al. Artificial Intelligence in Drug Discovery and Development: Transforming Pharmaceutical Innovation. Drug Development Research. 2026.](https://consensus.app/papers/details/7a8e73e032a7505fa67758810d3c03ec/) [5] [Fu C, et al. The future of pharmaceuticals: Artificial intelligence in drug discovery and development. Journal of Pharmaceutical Analysis. 2025.](https://consensus.app/papers/details/1308c134b9165903b7d81c4b8c636084/) [6] [Dermawan D, et al. From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes. Pharmaceuticals. 2025.](https://consensus.app/papers/details/12078be29bbb55428c31bf67e2b2f93a/) [7] [Yin T, et al. Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical applications. Frontiers in Molecular Biosciences. 2026.](https://consensus.app/papers/details/880e72fce35255c4887811c76849c03a/) [8] [Garcia M, et al. Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN. Protein Science. 2026.](https://consensus.app/papers/details/f9c8c442c8065c0688de2538546747db/) [9] [Schnapka V, et al. Atomic resolution ensembles of intrinsically disordered proteins with AlphaFold. Nature Communications. 2025.](https://consensus.app/papers/details/4f41871915fe5981b131bc6b01df2e2c/) [10] [Baek S, et al. Single-cell foundation models: bringing artificial intelligence into cell biology. Experimental & Molecular Medicine. 2025.](https://consensus.app/papers/details/e3168426724b5c3c8d867083d35fdc92/) [11] [Ji BY, et al. CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein. Nature Communications. 2026.](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/) [12] [Wang Y, et al. SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration. Molecular Therapy. 2026.](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/) [13] [Mirakhori F, et al. Harnessing the AI/ML in drug and biological products discovery and development: the regulatory perspective. Pharmaceuticals. 2025.](https://consensus.app/papers/details/80814775998c5bd9af2a78b64f1851a6/) [14] [Mao Z, et al. Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation. Frontiers in Pharmacology. 2026.](https://consensus.app/papers/details/ca5321af0bb656e095b6108766231764/) [15] [Ali M, et al. Artificial Intelligence in Drug Discovery and Development: Transforming Pharmaceutical Innovation. Drug Development Research. 2026.](https://consensus.app/papers/details/7a8e73e032a7505fa67758810d3c03ec/) [16] [Li J, et al. Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment. Cell Reports Medicine. 2026.](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/) [17] [Gao S, et al. A spatially resolved atlas of gastric cancer characterises a lymphocyte-aggregated region. Nature Communications. 2026.](https://consensus.app/papers/details/46f3d71acd2e5b6e8816d6886aa485d9/) [18] [Nguyen H, et al. Spatial omics for profiling the dynamic tumor microenvironment. Clinical & Translational Immunology. 2026.](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/)
Research Pulse·2026-10-18
Research Pulse — 2026-10-18
28 journals × 7 topics · fibrosis · OXPHOS · ferroptosis · sarcopenia · senescence
Raw data + scoring: Daily Tech Digest (separate feed)
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