A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

HTSeq—a Python framework to work with high-throughput sequencing data

Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139

The igraph software package for complex network research

Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078

Molecular properties that influence the oral bioavailability of drug candidates

Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420

Lead- and drug-like compounds: the rule-of-five revolution

Lipinski, C. A. (2004) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2004.11.007 · Cited by 6,455

The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans

The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062

Effect of Rosiglitazone on the Risk of Myocardial Infarction and Death from Cardiovascular Causes

Nissen, S. E., & Wolski, K. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa072761 · Cited by 4,675

A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases

Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1998) · Journal of Combinatorial Chemistry · doi:10.1021/cc9800071 · Cited by 3,095

Clinical development success rates for investigational drugs

Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., & Rosenthal, J. (2014) · Nature Biotechnology · doi:10.1038/nbt.2786 · Cited by 2,536

Quest for discovering novel CDK12 inhibitor

Debnath, A., Singh, R. K., Mazumder, R., Mazumder, A., Srivastava, S., Chaudhary, H., Mangal, S., Sanchitra, J., Tyagi, P. K., Singh, S. K., et al. (2024) · Journal of Receptors and Signal Transduction · doi:10.1080/10799893.2024.2441185 · Cited by 3

Machine Learning: A Tool to Shape the Future of Medicine

Hazapi, O., Lаgopati, N., Pezoulas, V. C., Papayiannis, G. I., Fotiadis, D. I., Skaltsas, D., Vergetis, V., Tsirigos, A., Stratis, I. G., Yannacopoulos, A. N., et al. (2022) · Studies in big data · doi:10.1007/978-981-16-9158-4_12 · Cited by 3

Artificial intelligence in small-molecule drug discovery

Martinelli, C., Repetto, M., & Curigliano, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00012-0 · Cited by 3

Predicting clinical trial success for Clostridium difficile infections based on preclinical data

Li, F., Youn, J., Millsop, C., & Tagkopoulos, I. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1487335 · Cited by 2

TRANSFORMATIVE APPLICATIONS OF AI IN HEALTHCARE AND PHARMACEUTICAL INNOVATION

JASMINE, P., IBRAHIM, A., Siddiq, S., DHANURSHRA, A., NIRMALA, M., & BASHA, S. S. (2025) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2025v17i3.52719 · Cited by 2

Knowledge graph information bottleneck enhanced molecular representation learning

Dai, J., Fu, D., Qiu, Z., Ma, L., & Zhang, D. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107560 · Cited by 2

Trialblazer: A chemistry-focused predictor of toxicity risks in late-stage drug development

Zhang, H., Welsch, M., Schueller, W., & Kirchmair, J. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.118306 · Cited by 2

Multi-MoleScale: a multi-scale approach for molecular property prediction with graph contrastive and sequence learning

Lou, X., Cai, J., & Siu, S. W. I. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01126-w · Cited by 2

Update and ADMET Profile of the Latin American Natural Product Database: LANaPDB

Gómez‐García, A., Lavecchia, M. J., Olmedo, D. A., Solís, P. N., & Medina‐Franco, J. L. (2025) · Molecular Informatics · doi:10.1002/minf.70013 · Cited by 2

Prediction of drug approvals in oncology using explainable artificial intelligence

Watanabe, T., Nemoto, S., Kato, H., & Isomura, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-31939-6 · Cited by 2

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Adil, M., Tiwari, P., & Kanwal, S. (2026) · Journal of Applied Toxicology · doi:10.1002/jat.70112 · Cited by 2

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Gao, C., Pradeepkumar, J., Das, T., Thati, S., & Sun, J. (2026) · Nature Health · doi:10.1038/s44360-026-00081-6 · Cited by 18

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Li, W., & Bischel, H. N. (2026) · Environmental Science & Technology Letters · doi:10.1021/acs.estlett.6c00172 · Cited by 2

AI Driven Computational Nanomedicine for Personalized Cancer Therapy

Farghama, K., Khan, M. S., Wajid, M., & Shaffique, S. (2026) · BioNanoScience · doi:10.1007/s12668-026-02681-8 · Cited by 2

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Kundu, A., Feijoo, F., Martínez, D. A., Hermosilla, M., & Matis, T. I. (2021) · Health Care Management Science · doi:10.1007/s10729-021-09584-y · Cited by 2

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Preto, A. J., Marques-Pereira, C., Baptista, S. J., Bueschbell, B., Barreto, C. A. V., Gaspar, A. T., Pinheiro, I., Pereira, N. N., Pires, M., Ramalhão, D., et al. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820472-6.00048-7 · Cited by 2

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Arivanantham, S., Sekar, P., Sundhararajan, N., Nagalingam, Y., & Raju, S. K. (2023) · World Journal of Biology Pharmacy and Health Sciences · doi:10.30574/wjbphs.2023.16.2.0467 · Cited by 2

Artificial intelligence in cancer research and precision medicine

Corti, C., Cobanaj, M., Criscitiello, C., & Curigliano, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00005-3 · Cited by 3

Semi-supervised learning-based virtual adversarial training on graph for molecular property prediction

Lu, Y., Wang, C., Wang, Z., Zhang, X., Zhou, G., & Li, C. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.11.110 · Cited by 3

Tachykinin/neurokinin 3 receptor antagonists: In silico ADME/T analysis of novel compounds for menopause hot flashes therapy

Barrero, J. A., & Clavijo, A. ́. G. (2022) · Revista Colombiana de Ciencias Químico Farmacéuticas · doi:10.15446/rcciquifa.v51n1.102679 · Cited by 2

Artificial Intelligence (AI) in Drug Discovery and Medicine

Varun, A. (2019) · Journal of Clinical Cases & Reports · doi:10.46619/joccr.2019.2-1043 · Cited by 2

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Huang, J., & Li, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-819872-8.00012-4 · Cited by 2

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Kantak, M., & Shende, P. K. (2023) · Toxicology Research · doi:10.1093/toxres/tfad001 · Cited by 1

Computational Approaches: A New Frontier in Cancer Research

Srivastava, S., & Jain, P. (2023) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/0113862073265604231106112203 · Cited by 1

Molecular representation learning based on Transformer with fixed-length padding method

Wu, Y., Yang, Y., Zhang, R., Chen, Z., Jin, M., Zou, Y., Wang, Z., & Wu, F. (2024) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2024.139574 · Cited by 1

Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls

Bhatt, J. A., Morris, K. R., & Haware, R. V. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02970-z · Cited by 1

AGPred: An End-to-End Deep Learning Model for Predicting Drug Approvals in Clinical Trials Based on Molecular Features

Zhao, H., Liang, X., Xie, C., & Wang, S. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3547315 · Cited by 1

Artificial Intelligence for Clinical Trial Facilitation, Lessons for Inflammatory Bowel Disease: A Scoping Review

Berre, C. L., Jairath, V., Panaccione, R., Bourreille, A., Magro, F., Danese, S., & Peyrin‐Biroulet, L. (2025) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2025.01.030 · Cited by 2

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Chung, Y.-H., Tung, C.-W., & Chang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8889-0_35 · Cited by 1

Comparative analysis of the chemical composition and cytotoxic activity of broccoli by-products as an alternative source of bioactive compounds

Guerrero‐Alonso, A., González‐Maya, L., Sánchez‐Carranza, J. N., Marbán‐González, A., Marquina, S., Álvarez, L., & Antunez‐Mojica, M. (2025) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2025.102200 · Cited by 1

Deep learning for toxicity prediction

Miao, Y., & Huang, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-332908-1.00028-3 · Cited by 1

Wellbeing for staff in UKCRC-registered Clinical Trials Units: Development of the Flourishing As Clinical Trial Staff (FACTS) guidance: a mixed-methods study

Hall, S. S., Riga, E., Mitchell, E. J., Thomson, L., Taylor, J., Carr, L., Hagan, P., & Sprange, K. (2025) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2025.101556 · Cited by 1

Feature-enhanced graph neural network with multiple attention for molecular property prediction

Qin, B., Zhu, X., Fan, C.-Y., Xue, X., Wang, M., & Tang, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132426 · Cited by 1

gSelformer-MV: Multiview, Subgraph-Augmented Group SELFIES Transformer for Molecular Property Prediction

Korolev, V., Sorokin, A. A., & Kuratov, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02342 · Cited by 1

Transforming drug development with AI-driven models

Pathak, S., & Garg, N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00008-7 · Cited by 1

AI-designed drugs: clinical development and future directions

Ahmed, R., Akhtar, S., Das, A., Begum, K., Sarma, A., Chakraborty, A., Neog, T., & Banu, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00010-5 · Cited by 1

Success rates of American clinical oncology trials by geographic factors

Patiyal, S., & Schäffer, A. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-39609-x · Cited by 1

Multi-modal graph neural networks with cross-view attention and contrastive learning for molecular property prediction

Liu, Z., Zhou, S., Liu, T., Liu, C. X., Zang, M., & Wang, Q. (2026) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2026.2661853 · Cited by 1

Functional Groups Are All You Need for Chemically Interpretable Molecular Property Prediction

Balaji, R., Bobby, J., & Bhatt, N. P. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02769 · Cited by 3

Forecasting novel therapeutic development in biomedical research

Arabi, S., & Hutchins, B. I. (2026) · Communications Medicine · doi:10.1038/s43856-026-01838-6 · Cited by 4

Artificial neural network–based inference of drug–target interactions

Melamane, S., Mandava, T. T., Manda, A., Luphade, L. N., Khamanga, S. M., Makoni, P. A., Demana, P. H., Matafwali, S. K., & Witika, B. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91763-6.00015-1 · Cited by 2

Advancing the Boundary of Pre-Trained Models for Drug Discovery: Interpretable Fine-Tuning Empowered by Molecular Physicochemical Properties

Pan, X., Lian, X., Zhu, J., Lv, T., Hong, X., Ding, L., Ni, J., Jiang, C., & Ni, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3416348 · Cited by 1

MulAFNet: Integrating Multiple Molecular Representations for Enhanced Property Prediction

Ci, L., Li, B., Xu, J., Peng, S., Jiang, L., & Long, W. (2025) · ACS Omega · doi:10.1021/acsomega.4c09884 · Cited by 6

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Silva, O. N., Neves, B. J., Rosseto, L. P., de Moura, R. S., Napolitano, H. B., Brito, W. D. A., Fajemiroye, J. O., Pinto, E., Perjési, P., & Martins, J. L. R. (2021) · Fronteiras Journal of Social Technological and Environmental Science · doi:10.21664/2238-8869.2021v10i1.p241-270

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Demirel, M. F., Liu, S., Garg, S., & Liang, Y. (2021) · arXiv (Cornell University) · Cited by 1

Faculty Opinions recommendation of A deep learning approach to antibiotic discovery.

Watkins, R. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.737402248.793572001 · Cited by 1

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Huang, C.-H., Hsu, J., Yang, L.-Y., Chen, T., Shih, E. S. C., & Hwang, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.15.512330 · Cited by 1

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Sun, H., Jiang, Y., Wang, M., Xiao, X., Wan, X., Tang, M., Jin, X., Niu, Z., & Chen, H. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-3b0js · Cited by 1

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Qian, L., Lu, X., Haris, P. I., Shuo, L., Zhu, J., & Yang, Y. (2025) · medRxiv · doi:10.1101/2025.07.06.25330956 · Cited by 1

Structure-based analysis of curcumin and conventional drugs targeting tumor-inducing protein PHF20

Agrawal, V., Mishra, A., Tiwari, S., & Srivastava, A. K. (2018) · Bioinformation · doi:10.6026/97320630014477

Transforming Drug Discovery for Aging Research with AI-Based Integrated Approaches

Vanhaelen, Q. (2020) · Advances in Geriatric Medicine and Research · doi:10.20900/agmr20200010

Aplicação de algoritmos de machine learning na avaliação do consumo alimentar: resultados da linha de base do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil)

da Silva, V. C. (2021) · doi:10.11606/t.6.2021.tde-23042021-163016

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Hao, Y., Thangaraj, P., & Tatonetti, N. P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.07.471637

Faculty Opinions recommendation of A deep learning approach to antibiotic discovery.

Swinney, D. C. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.737402248.793576936

Faculty Opinions recommendation of Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects.

Babu, M. M., & Marti, M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735476043.793566834

Faculty Opinions recommendation of Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects.

Flannick, J. A. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735476043.793563024

Drug approval prediction based on the discrepancy between gene perturbation effects in cells and humansPreprint

Kim, S., Park, M., Kim, D., Kim, I., & Im, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1932224/v1

Biología de sistemas y computacional en cáncer: de lo básico a la próxima frontera

Cardona, A. F., Pino, L. E., & Cantor, E. A. (2022) · Medicina · doi:10.56050/01205498.1651

Novel Methodology for Improving the Generalization Capability of Chemo-Informatics Deep Learning Models

Sandjakoska, L., Bogdanova, A. M., & Pejov, L. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-22792-9_13

A Novel Atom Pair Attention Methodology for Molecular Representation LearningPreprint

Sun, H., Jiang, Y., Wang, M., Xiao, X., Wan, X., Tang, M., Jin, X., Niu, Z., & Chen, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2418652/v1

The Structure-property Relationships of Clinically Approved Protease Inhibitors

Choi, K. (2023) · Current Medicinal Chemistry · doi:10.2174/0929867330666230409232655 · Cited by 1

Artificial Intelligence used in Pharmaceutical and Healthcare Industry: A Review

Goswami, S., & Singh, M. K. (2023) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-12471 · Cited by 2

Hardware/Software Coexploration for Hyperdimensional Computing on Network-on-Chip Architecture

Yang, J., & Yang, L. (2023) · IEEE Design and Test · doi:10.1109/mdat.2023.3309733

Advancing the Boundary of Pre-trained Models for Drug Discovery:Interpretable Fine-Tuning Empowered by Molecular Physicochemical PropertiesPreprint

Pan, X., Lian, X., Zhu, J., Lv, T., Hong, X., Ding, L., Li, L., Jiang, C., & Ni, J. (2023) · Research Square · doi:10.21203/rs.3.rs-3488751/v1

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Feng, Z., Yao, S., Song, J., Jia, L., Cheng, L., Zhong, Z., & Song, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3565603/v1

Integrating Molecular Graphs and Fingerprints through Contrastive Learning for Enhanced Molecular Property PredictionPreprint

Wei, L., Ci, L., Hu, L., Xu, J., Peng, S., & Jiang, L. (2023) · Research Square · doi:10.21203/rs.3.rs-3690402/v1

A COMPREHENSIVE REVIEW: EXPLORING ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY AND DEVELOPMENT

(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets48342

Artificial Intelligence for Drug Toxicity and Safety

Joshi-Barr, S., & Wampole, M. E. (2023) · Springer eBooks · doi:10.1007/978-3-030-73317-9_134-1

Analysing User Vulnerability to Phishing attacks using Machine Learning Techniques: A Case Study on University Data

Kumar, A., Prasad, S., & Kumar, K. N. P. (2023) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde59766.2023.10487734

Facilitating Feature Selection and Extraction in Clinical Trials with Large Language Models

Guo, J., Sun, W., Wen, S., Wu, D., & Zhou, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0840-9_16

Comprehensive Analysis of Masking Techniques in Molecular Graph Representation Learning

Koo, B., & Kwon, S. (2025) · IEEE Access · doi:10.1109/access.2025.3531302 · Cited by 1

A two-stage framework by leveraging large language model for predicting clinical trial outcomes

Kan, B., Zhu, H., Weng, H., Zeng, K., Wang, F. L., & Hao, T. (2025) · Intelligent Data Analysis · doi:10.1177/1088467x251330470

Enhancing Molecular Contrastive Prediction by Recognizing Metric Distribution Separation

Liu, X., & Sun, D. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3580460

Scaling Molecular Representation Learning with Hierarchical Mixture-of-ExpertsPreprint

Zhang, X., Yu, S., Xia, J., & Yang, F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.09.669511

FusionCLM: enhanced molecular property prediction via knowledge fusion of chemical language models

Lu, Y., Li, Y. Y., Sun, Y., & Hu, P. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01073-6

PyaiVS unifies AI workflows to accelerate ligand discovery and yields ABCG2 inhibitors

Wang, M., Qu, B., Yang, L., Wang, L., Jiang, K., & Lin, J. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.118176 · Cited by 1

Ontology pre-training improves machine learning-based predictions for metabolitesPreprint

Tumescheit, C., Glauer, M., Flügel, S., Larralde, M. F., Neuhaus, F., Mossakowski, T., & Hastings, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.30.679573

Implementation of Firefly Algorithm-Support Vector Machine in Classifying Clinical Trial Toxicity

Mahardika, R. A., & Kurniawan, I. (2025) · doi:10.1109/icoict66265.2025.11193023

Additional file 3 of Machine learning prediction of oncology drug targets based on protein and network properties

Dezső, Z., & Ceccarelli, M. (2020) · Figshare · doi:10.6084/m9.figshare.11986335

Additional file 1 of Machine learning prediction of oncology drug targets based on protein and network properties

Dezső, Z., & Ceccarelli, M. (2020) · Figshare · doi:10.6084/m9.figshare.11986329

Additional file 2 of Machine learning prediction of oncology drug targets based on protein and network properties

Dezső, Z., & Ceccarelli, M. (2020) · Figshare · doi:10.6084/m9.figshare.11986332

Additional file 1 of ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction

Liu, C., Sun, Y., Davis, R. L., Cardona, S. T., & Hu, P. (2024) · Figshare · doi:10.6084/m9.figshare.26571487

Contrastive Learning Approaches for Drug Discovery

Sanchez-Fernandez, A., & Klambauer, G. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_15

MMPCS: multi-view molecular pretraining based on consistency information and specific information

Xie, C., Song, Y., He, S., Bo, X., & Zhang, Z. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag028

Integrative machine learning and structure-based computational modeling reveal germination-driven enrichment of bioactive dipeptides in pigmented rice

Guzman, C. D. I. D., Perfinian, G. C., Alseekh, S., Fernie, A. R., Tiozon, R. N., & Sreenivasulu, N. (2026) · Food Bioscience · doi:10.1016/j.fbio.2026.108610

Dual-stage learning framework fusing semantics and physical knowledge for molecular property prediction

Zhuang, L., Sun, Y., Zhao, J., & Wang, H. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115106

Machine Learning-Based Soft Voting Ensemble Model for the Prediction of Oral Drug-Likeness of Chemical Structures

Petrosyan, N., Matevosyan, H., Tirosyan, I., Gabrielyan, Y., Gevorgyan, S., Harutyunyan, V., Matevosyan, M., & Zakaryan, H. S. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02953

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Singh, P. K., Kumar, I., Kumar, U., Singh, R. P., Singh, R. S., & Sharma, R. K. (2026) · Environmental Pollution · doi:10.1016/j.envpol.2026.128415

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Mena, H., Blaskovits, J. T., Lin, K., & Andrienko, D. (2026) · Advanced Materials · doi:10.1002/adma.202523667

Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training DatasetsPreprint

Chain, C., Ghaffari, S., Belakaria, S., Sheehan, J. P., Irani, I., Wu, C.-Y., Kim, H., Engelhardt, B. E., & Gitai, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.08.730938

Conditional disentangled information bottleneck for multi-view molecular graph representation learning

Dai, J., Fu, D., Qiu, Z., Ma, L., & Zhang, D. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133312

Integrating machine learning and pathway modelling to explore factors associated with chronic post-surgical pain and quality of life: a secondary observational analysis of the ENIGMA-II trial

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