ArticleCPT: pharmacometrics & systems pharmacology2020
Machine Learning in Drug Discovery and Development Part 1: A Primer.
Article in CPT: pharmacometrics & systems pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment.CPT: pharmacometrics & systems pharmacology · 2022Trial
- From Small Data to Big Decisions: How Clinical Pharmacology Shapes Rare Disease Development.Journal of clinical pharmacology · 2026Review
- Integrated Screening Cascades for Ion-Channel Drug Discovery: Linking Structure, Electrophysiology, Safety Pharmacology, and Human-Relevant Models.International journal of molecular sciences · 2026Review
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- Quantitative Systems Pharmacology Modeling Amid the Rise of Agentic AI.CPT: pharmacometrics & systems pharmacology · 2026Article
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.CPT: pharmacometrics & systems pharmacology · 2025Article
- Integrating QSP and ML to Facilitate Drug Development and Personalized Medicine.Handbook of experimental pharmacology · 2025Review
- Substituted 1,4-naphthoquinones for potential anticancer therapeutics:Computational and structural biotechnology journal · 2025Article
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- Artificial Intelligence and Machine Learning Applications to Pharmacokinetic Modeling and Dose Prediction of Antibiotics: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation.Biomolecules · 2024Article
- Article
- Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.Life (Basel, Switzerland) · 2024Review
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images.Pharmaceutics · 2023Article
- Review of Predicting Synergistic Drug Combinations.Life (Basel, Switzerland) · 2023Review
- Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics.Molecular aspects of medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.