ArticleChemical science2019
DeltaDelta neural networks for lead optimization of small molecule potency.
Article in Chemical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
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.
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Who cites it
20 citing papers in PubMed.
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.Journal of advanced research · 2025Article
- Directional ΔJournal of chemical information and modeling · 2024Article
- Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.Molecules (Basel, Switzerland) · 2024Article
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
- DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.Journal of cheminformatics · 2023Article
- Potent antibiotic design via guided search from antibacterial activity evaluations.Bioinformatics (Oxford, England) · 2023Article
- Review
- Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery.Molecules (Basel, Switzerland) · 2022Review
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- ImprovingJournal of chemical information and modeling · 2022Review
- Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1].Living journal of computational molecular science · 2022Article
- Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators.RSC medicinal chemistry · 2021Review
- DeepFrag: a deep convolutional neural network for fragment-based lead optimization.Chemical science · 2021Article
- Siamese Recurrent Neural Network with a Self-Attention Mechanism for Bioactivity Prediction.ACS omega · 2021Article
- Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction.Interface focus · 2020Review
- A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection.Molecules (Basel, Switzerland) · 2020Article
- libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications.Journal of chemical information and modeling · 2020Article
- graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein-Ligand Complexes.ACS omega · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives.
Identifiers
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.