From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes
Abstract
1. Introduction
2. Results
2.1. Systematic Literature Search and Study Selection Workflow
2.2. Distribution of AI Applications Across Drug Development Stages, Geographic Trends, Industry Collaboration, and AI Technology Adoption
2.3. Landscape of AI Applications in Pharmaceutical R&D: Trends and Case Studies
3. Discussion
3.1. Main Findings and Comparison with Prior Works
3.2. Limitations and Future Works
4. Materials and Methods
4.1. Eligibility Criteria
4.2. Information Sources
4.3. Search Strategy
4.4. Study Selection
4.5. Data Extraction
4.6. Data Synthesis and Interpretations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ADMET | Absorption, distribution, metabolism, excretion, and toxicity |
AI | Artificial intelligence |
AML | Acute myeloid leukemia |
CV | Computer vision |
DDD | Drug discovery and development |
DL | Deep learning |
GM | Generative model |
HGSOC | High-grade serous ovarian cancer |
HTS | High-throughput screening |
IBD | Inflammatory bowel disease |
IND | Investigational new drug |
IPF | Idiopathic pulmonary fibrosis |
KG | Knowledge graph |
KPIs | Key performance indicators |
ML | Machine learning |
MMS | Molecular modeling and simulation |
NLP | Natural language processing |
NSCLC | Non-small cell lung cancer |
OCD | Obsessive–compulsive disorder |
OM | Omics integration |
PBM | Physics-based modeling |
PK/PD | Pharmacokinetics/pharmacodynamics |
PRISMA | Preferred reporting items for systematic reviews and meta-analyses |
QSAR | Quantitative structure–activity relationship |
R&D | Research and development |
RL | Reinforcement learning |
SBDD | Structure-based drug design |
SLE | Systemic lupus erythematosus |
TNBC | Triple-negative breast cancer |
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Study; Year | AI Technique Used | Drug Discovery Stage | Therapeutic Area | Company/Funding Source |
---|---|---|---|---|
Dumbrava, EE. et al. [43]; 2024 | Structure-based drug design, virtual screening, ADMET prediction, biomarker identification | Clinical Phase I | Oncology (TNBC, HGSOC, endometrial cancer with TP53 mutation/loss) | A2A Pharmaceuticals |
Patel, MR. et al. [44]; 2024 | ChemiRise, orbital virtual screening, intelligent SAR, Chemi-Net (AI-driven computational drug design, virtual screening, PK/PD prediction) | Clinical Phase I | Oncology (ER+/HER2- Breast Cancer) | Accutar Biotechnology Inc. |
Niewiarowska, A. et al. [45]; 2017 | BERG’s Interrogative Biology® platform + Oak Ridge Frontier supercomputer: Bayesian AI modeling, Multi-Omics data integration, supercomputing | Clinical Phase II | Oncology (Pancreatic Cancer) | BERG LLC. |
Xia, S. et al. [46]; 2022 | DL for epitope mapping and functional screening, synthetic antigen design, multi-modal AI for antibody optimization | Preclinical | Oncology (HER2+ Breast Cancer) | Baseimmune |
Molnar, J. et al. [47]; 2025 | Knowledge graph and ML for target prioritization | Clinical Phase I | Gastroenterology (Ulcerative Colitis) | BenevolentAI |
Hartman, G. et al. [48]; 2024 | DNA-encoded library (DEL) screening, computational modeling, and structure–activity relationship (SAR) analysis | Preclinical | Immunology (NLRP3-related) | BioAge Labs |
Risinger, R. et al. [49]; 2025 | NovareAI: Drug repurposing via big data integration, ML-based target identification, predictive modeling for trial design | Clinical Phase Ib/II | Neuropsychiatry (Dementia/Agitation) | BioXcel Therapeutics |
Rotta, M. et al. [50]; 2024 | AI-driven FUSION™ System: Target ID, molecular modeling, PK/PD modeling, biomarker stratification | Clinical Phase I | Oncology (Acute myeloid leukemia/AML) | Biomea Fusion Inc. |
Patel, J. et al. [51]; 2024 | AI-based MAP platform for mutation analysis, allosteric site prediction, compound optimization, PK/PD modeling | Clinical Phase II | Oncology (Glioblastoma, non-small cell lung cancer/NSCLC) | Black Diamond Therapeutics |
Idowu, O. et al. [52]; 2023 | AI-driven epitope prediction, multi-omic integration, biomarker stratification, and predictive modeling via RAD platform | Clinical Phase I/II | Oncology (Advanced solid tumors) | Cancer Research UK |
Grant, S. et al. [53]; 2023 | ML (Target identification from scRNA-seq data via SCOPE platform) | Preclinical to Clinical Phase I | Gastroenterology (Ulcerative Colitis and Crohn’s Disease) | Celsius Therapeutics |
Dumbrava, E. et al. [54]; 2021 | Unigen™: ML–based predictive target discovery, AI-driven antibody design, spatial transcriptomics integration, and combination therapy modeling | Clinical Phase I | Oncology (Advanced Solid Tumors) | Compugen Ltd. |
Khairnar, V. et al. [55]; 2023 | Flex-NK™ platform: Computational antibody design, gene expression profiling, in vitro and in vivo modeling, combination therapy optimization using AI-driven analyses and structural modeling | Preclinical | Oncology (Multiple Myeloma) | Cytovia Therapeutics |
Salto, MS. et al. [56]; 2024 | Generative AI for compound design, RL for chemical space exploration, physics-based simulations for binding affinity optimization, automated synthesis and screening | Preclinical | Immunology (Rheumatoid Arthritis) | DeepCure Inc. |
Xu, C. et al. [57]; 2023 | IDInVivo platform: AI-driven in vivo gene targeting, preclinical efficacy modeling, PK/PD prediction, biomarker identification | Preclinical | Infectious Diseases (Hepatitis B) | Drug Farm |
Wong, G [58]; 2024 | AI-driven target discovery (Precision Insights), siRNA design (siRCH), pharmacokinetics modeling, biomarker-based stratification | Preclinical to Clinical Phase I | Pulmonary (Chronic Lung Disease) | Empirico |
Khattak, A. et al. [59]; 2023 | AI-Immunology™ platform (PIONEER™): Neoantigen prediction, ML, immune response modeling | Clinical Phase II | Oncology (Melanoma) | Evaxion Biotech |
Diaz, N. et al. [60]; 2023 | Generative design, ML for predictive modeling, simulation-guided clinical trial design | Clinical Phase I | Oncology (Renal cell carcinoma/RCC, NSCLC) | Exscientia and Evotec |
Eckstein, F. et al. [61]; 2020 | AI-assisted MRI segmentation and quantitative MRI (qMRI) analysis, location-independent cartilage change analysis, post hoc data analysis | Clinical Phase II | Rheumatology (Knee Osteoarthritis) | Formation Bio |
Keating, AT. et al. [62]; 2024 | AI-driven chemoproteomics, Druggability Atlas™ construction, covalent fragment-based drug discovery, ML, predictive modeling of resistance mechanisms | Clinical Phase I/II | Oncology (KRASG12C Mutant Tumors: NSCLC, PDAC, CRC) | Frontier Medicines |
Wentzel, K. et al. [63]; 2024 | GV20’s STEAD platform: AI-driven target discovery, antibody sequence prediction, and functional genomics integration | Clinical Phase I/II | Oncology (Advanced solid tumors) | GV20 Therapeutics |
Guzman, B. et al. [64]; 2023 | Magellan™ AI platform for allosteric modulator discovery, structural modeling, predictive modeling (PK/PD), biomarker identification | Preclinical | Neurology (Parkinson’s Disease) | Gain Therapeutics |
Alwis, DD. et al. [65]; 2025 | ML (Generate Platform) + iterative computation-experimentation loop | Clinical Phase I | Infectious Diseases (COVID-19 prophylaxis) | Generate Biomedicines |
Spira, AI. et al. [66]; 2022 | Generative AI, multi-modal predictive modeling, convolutional neural networks | Clinical Phase I | Oncology (Solid tumors including EBV+ gastric cancer, ccRCC, melanoma, mesothelioma) | HiFiBiO Therapeutics |
Sanborn, RE. et al. [67]; 2024 | Smart Allostery™ platform: AI-driven data mining, computational modeling | Clinical Phase I/II | Oncology (Advanced solid tumors) | HotSpot Therapeutics |
Ahnert, JR. et al. [68]; 2023 | RAD platform (AI-driven epitope prediction), mAbPredictAI (AI-guided antibody design), cross-species AI analysis, systems biology integration | Clinical Phase I | Oncology (TNBC, NSCLC, other solid tumors) | Hummingbird Bioscience |
Adjei, AA. et al. [69]; 2024 | Iambic AI: Physics-informed AI drug discovery platform | Clinical Phase I/Ib | Oncology (HER2-driven solid tumors) | Iambic Therapeutics |
Ren, F. et al. [16]; 2025 | Chemistry42: Generative models and RL | Clinical Phase III | Pulmonary (Idiopathic pulmonary fibrosis/IPF) | Insilico Medicine |
Kim, H. et al. [70]; 2024 | AI-driven secretome mining, quantitative proteomics, and phenotypic validation | Preclinical | Endocrinology (Diabetes Type 1) | Juvena Therapeutics |
Leber, A. et al. [71]; 2023 | LANCE® AI Platform, TITAN-X AI Platform: ML, multiscale modeling, predictive analytics, and bioinformatics | Clinical Phase II | Gastroenterology (Ulcerative Colitis and Inflammatory Bowel Disease/IBD) | Landos Biopharma |
McKean, W. et al. [72]; 2024 | RADR® AI platform for identifying DNA repair vulnerabilities, biomarker signatures, and mechanism of action of LP-284 | Clinical Phase I/Ib | Oncology (Relapsed/Refractory B-cell NHL, Solid Tumors) | Lantern Pharma Inc |
Huang, Y. et al. [73]; 2024 | AiLNP (AI Lipid Nanoparticle) platform for lipid formulation optimization; AiTEM (AI Therapeutic Engine for mRNA) for mRNA therapeutic candidate optimization | Preclinical | Oncology (Hepatocellular Carcinoma) | METiS Pharmaceuticals |
Wang, S. et al. [74]; 2024 | DL, AI-based identification, and screening using IBM Watson | Preclinical | Infectious Diseases (Veterinary Bacterial Infections) | MIT and IBM Watson |
Verstockt, B. et al. [75]; 2024 | AI-powered precision medicine (TITAN-X Platform) for target discovery, biomarker identification, and trial optimization | Clinical Phase I/Ib | Gastroenterology (Ulcerative Colitis) | MedChemExpress, NIMML Institute |
Khanna, D. et al. [76]; 2024 | ML, QSAR models | Clinical Phase II | Dermatology (Skin diseases, autoimmune, fibrotic disorders) | Medi-Tate and Medidata AI |
Hussain, A. et al. [77]; 2025 | Schrödinger LiveDesign platform: Computational modeling, structural biology, ML | Clinical Phase IIa | Gastroenterology (Ulcerative Colitis) | Morphic Therapeutic, Schrödinger, Lilly |
Leber, A. et al. [78]; 2025 | TITAN-X Precision Medicine Platform: Gene expression analysis, Predictive modeling, multiomics data integration, mechanistic modeling, pharmacokinetic simulations | Clinical Phase I | Immunology (Systemic Lupus Erythematosus/SLE) | NImmune, MedPath, BioSpace |
Wu, R. et al. [79]; 2024 | neoBiologics™ and neoDegrader™ (AI for antibody design, protein degradation, PPI analysis, and immunogenicity prediction) | Preclinical to Clinical Phase I | Oncology (NSCLC, gastric, liver, esophageal tumors) | NeoX Biotech |
Noel, MS. et al. [80]; 2024 | Structure-based drug design, ML-based predictive modeling, medicinal chemistry optimization | Clinical Phase I/II | Oncology (Solid tumors) | Nimbus Therapeutics |
Papadopoulos, KP. et al. [81]; 2025 | AI-Driven Helicon design, computational physics integration, data science for trial optimization | Clinical Phase I/II | Oncology (Solid tumors) | Parabilis Medicines |
Shin, DY. et al. [82]; 2024 | AI-driven Chemiverse Platform: target identification, compound screening, ADMET Prediction | Clinical Phase I/II | Oncology (Acute Myeloid Leukemia) | Pharos iBio |
Alfa, R. et al. [83]; 2024 | Recursion OS: AI-driven drug discovery (DL, machine vision, predictive modeling, computational chemistry) | Clinical Phase I | Neurology (Cerebral Cavernous Malformations) | Recursion Pharmaceuticals |
Schönherr, H. et al. [84]; 2024 | Dynamo™ platform: Motion-based drug design (MBDD), molecular dynamics simulations, ML, AI-driven modeling | Clinical Phase I | Oncology (Solid Tumors, Intrahepatic Cholangiocarcinoma) | Relay Therapeutics |
Gamez, J. et al. [85]; 2023 | SOMAIPRO platform: AI-driven computational techniques to identify new mechanisms of action, predict drug-target interactions, and repurpose existing drugs for new indications | Clinical Phase II | Neurology (Huntington’s Disease) | SOM Biotech |
Krueger, JG. et al. [86]; 2024 | DL, molecular dynamics simulations, free energy perturbation (FEP) | Clinical Phase III | Dermatology (Psoriasis) | Schrödinger Inc. |
Manasson, J. et al. [87]; 2024 | IMPACT platform: ML-driven AI for IgG protease design, deimmunization (epitope elimination), pharmacokinetic/pharmacodynamic (PK/PD) modeling, and multi-mechanistic targeting for optimal drug performance | Preclinical | Immuno-oncology (Thrombocytopenia/ITP and Evans syndrome) | Seismic Therapeutic |
Rao, S. et al. [88]; 2023 | Pharma.AI: DL, RL, and generative chemistry | Preclinical | Oncology (Triple-negative breast cancer, B-cell non-Hodgkin lymphoma) | Shanghai Fosun Pharmaceutical Development Co. Ltd., Insilico Medicine |
Dedic, N. et al. [89]; 2019 | SmartCube® platform (phenotypic screening, computer vision, ML) | Preclinical to Clinical Phase I | Neurology (Schizophrenia) | Sumitomo Pharma and PsychoGenics |
Koblan, KS. et al. [90]; 2020 | SmartCube® platform (phenotypic screening, computer vision, ML) | Clinical Phase II | Neurology (Schizophrenia) | Sunovion Pharmaceuticals Inc. |
Sowell, RT. et al. [91]; 2023 | AI/ML-enabled target discovery, compound generation, and ADMET prediction | Preclinical | Oncology (Solid Tumors) | Supercede Therapeutics |
Fakih, M. et al. [92]; 2024 | DNA-encoded library screening, ML | Clinical Phase I | Oncology (Cancer) | Totus Medicines |
Criteria Type | Inclusion Criteria | Exclusion Criteria |
---|---|---|
Study Type | Peer-reviewed original research articles, white papers, or technical reports focusing on AI-driven drug discovery or development | Editorials, opinion pieces, reviews, commentaries, preprints, general reviews without AI focus, or unverified gray literature |
AI Method | Studies applying ML, DL, natural language processing, generative AI, RL, or knowledge graph-based models | Studies based solely on rule-based systems, deterministic algorithms, or expert systems without adaptive or learning capabilities |
Application Focus | Application of AI in drug discovery pipeline stages: target identification, hit/lead optimization, compound screening, preclinical evaluation, IND submission | Studies focused on AI in diagnostics, radiology, electronic health records, hospital operations, marketing, or unrelated computational biology applications |
Outcome Measures | Studies reporting on outcomes such as candidate nomination, time-to-lead, IND approval acceleration, development timeline reduction, or pipeline productivity | Studies lacking measurable outcomes or reporting only theoretical models without downstream drug development relevance |
Language | Published in English | Published in languages other than English |
Publication Date | Published between January 2015 and April 2025 | Published before January 2015 |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Dermawan, D.; Alotaiq, N. From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes. Pharmaceuticals 2025, 18, 981. https://doi.org/10.3390/ph18070981
Dermawan D, Alotaiq N. From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes. Pharmaceuticals. 2025; 18(7):981. https://doi.org/10.3390/ph18070981
Chicago/Turabian StyleDermawan, Doni, and Nasser Alotaiq. 2025. "From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes" Pharmaceuticals 18, no. 7: 981. https://doi.org/10.3390/ph18070981
APA StyleDermawan, D., & Alotaiq, N. (2025). From Lab to Clinic: How Artificial Intelligence (AI) Is Reshaping Drug Discovery Timelines and Industry Outcomes. Pharmaceuticals, 18(7), 981. https://doi.org/10.3390/ph18070981