ArticleACS medicinal chemistry letters2024
Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study.
Article in ACS medicinal chemistry letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
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Who cites it
5 citing papers in PubMed.
- Soft drugs: design principles and topical applications.Nature reviews. Drug discovery · 2026Review
- Is AI Capable of Real-World Drug Discovery?Journal of chemical information and modeling · 2026Article
- In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors.Journal of computer-aided molecular design · 2025Article
- Computational approaches for toxicology and Pharmacokinetic properties prediction.Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimization of the ADME properties and pharmacokinetic (PK) profile of compounds is one of the critical activities in any medicinal chemistry campaign to discover a future clinical candidate. Finding ways to expedite the process to address ADME/PK shortcomings and reduce the number of compounds to synthesize is highly valuable. This article provides practical guidelines and a case study on the use of ML ADME models to guide compound design in small molecule lead optimization. These guidelines highlight that ML models cannot have an impact in a vacuum: they help advance a program when they have the trust of users, are tuned to the needs of the program, and are integrated into decision-making processes in a way that complements and augments the expertise of chemists.
Identifiers
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Registered trials
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