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ZINC: A Free Tool to Discover Chemistry for Biology

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The rise of automated curiosity-driven discoveries in chemistry

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De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning

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Discovery of Potential Neonicotinoid Insecticides by an Artificial Intelligence Generative Model and Structure-Based Virtual Screening

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Discovery of 3-hydroxymethyl-azetidine derivatives as potent polymerase theta inhibitors

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DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation

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Academics as leaders in the cancer artificial intelligence revolution

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Innovative approaches in CNS drug discovery

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An efficient curriculum learning-based strategy for molecular graph learning

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Crowdsourcing and open innovation in drug discovery: recent contributions and future directions

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Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

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Discovery of Bis-imidazolecarboxamide Derivatives as Novel, Potent, and Selective TNIK Inhibitors for the Treatment of Idiopathic Pulmonary Fibrosis

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Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review

Qian, L., Lu, X., Haris, P. I., Zhu, J., Li, S., & Yang, Y. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104332 · Cited by 23

Chemical space visual navigation in the era of deep learning and Big Data

Sosnin, S. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104392 · Cited by 21

Artificial intelligence for radiopharmaceutical and molecular imaging

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A patent review of discoidin domain receptor 1 (DDR1) modulators (2014-present)

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Comprehensive assessment of deep generative architectures for de novo drug design

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Discoidin domain receptor 1 is a potential target correlated with tumor invasion and immune infiltration in gastric cancer

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Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

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De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder

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Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

Periwal, N., Arora, P., Thakur, A., Agrawal, L., Goyal, Y., Rathore, A. S., Anand, H. S., Kaur, B., & Sood, V. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36163 · Cited by 20

Language Models in Molecular Discovery

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Equivariant 3D-Conditional Diffusion Model for De Novo Drug Design

Jia, Z., Yi, H.-C., & You, Z. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3491318 · Cited by 23

Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

Loewe, A., Hunter, P. J., & Kohl, P. K. (2025) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2023.0384 · Cited by 19

Evaluating Deep Learning models for predicting ALK-5 inhibition

Espinoza, G. Z., de Angelo, R. M., Oliveira, P. R., & Honório, K. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246126 · Cited by 20

Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation

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AI-Driven De Novo Design and Development of Nontoxic DYRK1A Inhibitors

González, E., Varas, P., González-Naranjo, P., Ulzurrun, E., Marcos-Ayuso, G., Pérez, C., Paez, J. A., Insua, D. R., Santana, S. R., & Campillo, N. E. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00512 · Cited by 18

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

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LIMO: Latent Inceptionism for Targeted Molecule Generation

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Drug Chemical Space as a Guide for New Herbicide Development: A Cheminformatic Analysis

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Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform

Olsen, A., Harpaz, Z., Ren, C., Shneyderman, A., Veviorskiy, A., Dralkina, M., Konnov, S., Shcheglova, O., Pun, F. W., Leung, G. H. D., et al. (2023) · Aging · doi:10.18632/aging.204678 · Cited by 18

LS-MolGen: Ligand-and-Structure Dual-Driven Deep Reinforcement Learning for Target-Specific Molecular Generation Improves Binding Affinity and Novelty

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Deep learning assisted high throughput screening of ionic liquid electrolytes for NRR and CO2RR

Song, Y., Guo, Y., Chen, J., Yuan, M., & Dong, K. (2023) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2023.110556 · Cited by 19

Application of artificial intelligence and machine learning in drug repurposing

Ghandikota, S. K., & Jegga, A. G. (2024) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2024.03.030 · Cited by 24

De novo Molecular Design with Generative Long Short-term Memory

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Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques

Zha, J., Li, M., Kong, R., Lu, S., & Zhang, J. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167481 · Cited by 18

Laying the foundations for artificial intelligence in health

Hashiguchi, T. C. O., Slawomirski, L., & Oderkirk, J. (2021) · OECD health working papers · doi:10.1787/3f62817d-en · Cited by 28

Deep Learning and Site‐Specific Drug Delivery

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Integration of Federated Learning and Blockchain in Healthcare: A TutorialPreprint

Shahsavari, Y., Dambri, O. A., Baseri, Y., Hafid, A. S., & Makrakis, D. (2024) · doi:10.36227/techrxiv.171340632.25905526/v1 · Cited by 20

AI-Designed Molecules in Drug Discovery, Structural Novelty Evaluation, and Implications

Xie, S., Zhu, H., & Huang, N. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00921 · Cited by 22

Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis

Yang, L., Zhu, L., Zhang, S., & Hong, X. (2022) · Chinese Journal of Chemistry · doi:10.1002/cjoc.202200039 · Cited by 17

Automated flow synthesis of algorithmically designed ferroelectric nematogens

Mandle, R. J. (2023) · Liquid Crystals · doi:10.1080/02678292.2023.2208550 · Cited by 17

A community effort in SARS‐CoV‐2 drug discovery

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Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

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Deep learning in computer-aided drug design: a case study

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Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases

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Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design

Quinn, T. R., Giblin, K. A., Thomson, C., Boerth, J. A., Bommakanti, G., Braybrooke, E. L., Chan, C., Chinn, A. J., Code, E. L., Cui, C., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01034 · Cited by 19

Deep-Learning-Driven Discovery of SN3–1, a Potent NLRP3 Inhibitor with Therapeutic Potential for Inflammatory Diseases

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Discovery of Pyrrolopyrazine Carboxamide Derivatives as Potent and Selective FGFR2/3 Inhibitors that Overcome Mutant Resistance

Wang, Y., Zhang, Y., Liu, J., Zhao, J., Wang, C. W. C., Meng, F., Cai, X., Zhang, M., Aliper, A. M., Liang, T., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c03205 · Cited by 17

DrugDiff: small molecule diffusion model with flexible guidance towards molecular properties

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Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein–Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Kralj, S., Jukič, M., & Bren, U. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms23010393 · Cited by 17

Next Decade’s AI-Based Drug Development Features Tight Integration of Data and Computation

Luo, Y., Peng, J., & Ma, J. (2022) · Health Data Science · doi:10.34133/2022/9816939 · Cited by 17

Role of Artificial Neural Networks in Pharmaceutical Sciences

Teja, T. B., Sekar, M., Pallavi, T. P., Mettu, S., Murthy, T. E. G. K., Rani, N. N. I. M., Bramhachari, P. V., & Bonam, S. R. (2022) · Journal of Young Pharmacists · doi:10.5530/jyp.2022.14.2 · Cited by 19

Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications

Qasim, H. S., & Simpson, M. D. (2025) · Pharmacy · doi:10.3390/pharmacy13020041 · Cited by 27

SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models

Sun, K., Bagni, D., Cavanagh, J. M., Wang, Y., Sawyer, J. M., Zhou, B., Gritsevskiy, A., Zhang, O., & Head-Gordon, T. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c01285 · Cited by 33

Mitigating patient harm risks: A proposal of requirements for AI in healthcare

Garcia-Gomez, J. M., Blanes-Selva, V., Álvarez-Romero, C., Cenzano, J. C. D. B., Mesquita, F. P., Pazos, A., & Doñate-Martínez, A. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103168 · Cited by 19

Deep‐learning‐based line enhancer for passive sonar systems

Ju, D., Chi, C., Li, Z., Li, Y., Zhang, C., & Huang, H. (2021) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.12205 · Cited by 16

De Novo design of potential inhibitors against SARS-CoV-2 Mpro

Li, S., Wang, L., Meng, J., Zhao, Q., Zhang, L., & Liu, H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105728 · Cited by 16

Discovery of TIGIT inhibitors based on DEL and machine learning

Xiong, F., Yu, M., Xu, H., Zhong, Z., Li, Z., Guo, Y., Zhang, T., Zeng, Z., Jin, F., & He, X. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.982539 · Cited by 15

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration

Krishnan, K., Kassab, R., Agajanian, S., & Verkhivker, G. M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231911262 · Cited by 16

Uncovering the pharmacology of Ginkgo biloba folium in the cell-type-specific targets of Parkinson’s disease

Yan, Y., Xu, Z., Wang, J., & Yu, W. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1007556 · Cited by 16

PCW-A1001, AI-assisted de novo design approach to design a selective inhibitor for FLT-3(D835Y) in acute myeloid leukemia

Jang, S. H., Sivakumar, D., Mudedla, S. K., Choi, J., Lee, S., Jeon, M., Suneel Kumar, B. V. S., Hwang, J., Kang, M., Shin, E. G., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.1072028 · Cited by 16

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science

Stuart, D. D., Guzman-Perez, A., Brooijmans, N., Jackson, E. L., Kryukov, G. V., Friedman, A. A., & Hoos, A. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0280 · Cited by 19

Complexity of life sciences in quantum and AI era

Pyrkov, A. N., Aliper, A., Bezrukov, D. S., Podolskiy, D. I., Ren, F., & Zhavoronkov, A. A. (2024) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1701 · Cited by 18

THE IMPACT OF AI INNOVATION MANAGEMENT ON ORGANIZATIONAL PRODUCTIVITY AND ECONOMIC GROWTH: AN ANALYTICAL STUDY

Yi, Z., & Ayangbah, S. (2024) · International journal of business management and economic review · doi:10.35409/ijbmer.2024.3580 · Cited by 19

Targeting PI3K Signaling to Overcome Tumor Immunosuppression: Synergistic Strategies to Enhance Cancer Vaccine Efficacy

Cui, R., Luo, Z., Zhang, X., Yu, X., Yuan, G., Li, X., Xie, F., & Jiang, O. (2025) · Vaccines · doi:10.3390/vaccines13030292 · Cited by 16

Systematic risk identification and assessment using a new risk map in pharmaceutical R&D

Schuhmacher, A., Brieke, C., Gassmann, O., Hinder, M., & Hartl, D. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.06.015 · Cited by 17

Current methods and challenges for deep learning in drug discovery

Schroedl, S. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.07.003 · Cited by 18

The Utility of Generative AI in Advancing Global Health

Akbarialiabad, H., Sadeghian, N., Haghighat, S., Grada, A. A., Paydar, S., Haghighi, A., Kvedar, J. C., & Sewankambo, N. K. (2025) · NEJM AI · doi:10.1056/aip2400875 · Cited by 16

Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects

Bassey, G. E., Daniel, E., Okesina, K. B., & Odetayo, A. F. (2025) · Drug Design Development and Therapy · doi:10.2147/dddt.s538269 · Cited by 22

Artificial Intelligence Driven Framework for the Design and Development of Next-Generation Avian Viral Vaccines

Goud, M. D., Ramos, E., Shah, A. U., & Hemida, M. G. (2025) · Microorganisms · doi:10.3390/microorganisms13102361 · Cited by 15

The paradigm shift in therapeutics: a comprehensive review of artificial intelligence in drug delivery systems

Singh, S., & De, A. (2026) · RSC Pharmaceutics · doi:10.1039/d5pm00235d · Cited by 15

Adaptive Deep Convolutional GAN for Fingerprint Sample Synthesis

Striuk, O., & Kondratenko, Y. P. (2021) · IEEE International Conference on Advanced Information and Communication Technologies (AICT) · doi:10.1109/aict52120.2021.9628978 · Cited by 15

Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery

Wang, A., & Durrant, J. D. (2022) · Molecules · doi:10.3390/molecules27144623 · Cited by 15

De NovoDesign of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2

Staker, J., Marshall, K., Leswing, K., Robertson, T., Halls, M. D., Goldberg, A., Morisato, T., Maeshima, H., Ando, T., Arai, H., et al. (2022) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c04221 · Cited by 14

ReBADD-SE: Multi-objective molecular optimisation using SELFIES fragment and off-policy self-critical sequence training

Choi, J., Seo, S., Choi, S., ‍Piao, S., Park, C., Ryu, S. J., Kim, B. J., & Park, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106721 · Cited by 15

Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy

Kozlov, K. S., Boiko, D. A., Detusheva, E. V., Detushev, K. V., Pentsak, E. O., Vereshchagin, A. N., & Ananikov, V. P. (2023) · Digital Discovery · doi:10.1039/d3dd00048f · Cited by 15

ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation

Kyro, G. W., Morgunov, A., Brent, R. I., & Batista, V. S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01456 · Cited by 18

Advances in Deep Learning Assisted Drug Discovery Methods: A Self-review

Zhang, H., & Saravanan, K. M. (2024) · Current Bioinformatics · doi:10.2174/0115748936285690240101041704 · Cited by 16

Research progress of DDR1 inhibitors in the treatment of multiple human diseases

Liu, M., Zhang, J., Li, X., & Wang, Y. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.116291 · Cited by 15

Discovery of a Novel and Potent Cyclin-Dependent Kinase 8/19 (CDK8/19) Inhibitor for the Treatment of Cancer

Xu, J., Qi, H., Wang, Z., Wang, L., Steurer, B., Cai, X., Liu, J., Aliper, A., Zhang, M., Ren, F., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c00248 · Cited by 15

GENERATIVE AI IN HEALTHCARE: REVOLUTIONIZING DISEASE DIAGNOSIS, EXPANDING TREATMENT OPTIONS, AND ENHANCING PATIENT CARE

Abbasi, N., Nizamullah, F. N. U., Zeb, S., & Fardous, M. (2024) · Journal of Knowledge Learning and Science Technology · doi:10.60087/jklst.vol3.n3.p127-138 · Cited by 15

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

Yadav, M. K., Dahiya, V., Tripathi, M. K., Chaturvedi, N., Rashmi, M., Ghosh, A., & Raj, V. S. (2024) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2024.177103 · Cited by 19

New methodologies in ageing research

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Generative Adversarial Neural Network for Creating Photorealistic Images

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