ArticleJournal of cheminformatics2020
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- PockLigGPT: Pocket-Sequence-Conditioned Molecular Generation with GPTs and RL.Journal of chemical information and modeling · 2026Article
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- Bridging the phenotype-target gap for molecular generation via multi-objective reinforcement learning.Bioinformatics (Oxford, England) · 2026Article
- 3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table.Journal of chemical information and modeling · 2026Article
- A Transformer for Reaction-Aware Compound Explorations with GFlowNet in QSAR-Guided Molecular Design.Journal of chemical information and modeling · 2026Article
- Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning.Journal of chemical theory and computation · 2026Article
- Generative AI in structure-based drug discovery.BMC biology · 2026Review
- Precision fragment addition: domain-specific DeepFrag2 models for smarter lead optimization.Digital discovery · 2026Article
- Frags2Drugs: A Novel In Silico Fragment-Based Approach to the Discovery of Kinase Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- MolOrgGPT:Journal of chemical information and modeling · 2026Article
- Computational Design of Drugs for Epilepsy using a Novel Guided Evolutionary Algorithm for Enhanced Blood Brain Barrier Permeability.Central nervous system agents in medicinal chemistry · 2026Article
- Deep learning reveals endogenous sterols as allosteric modulators of the GPCR-Gα interface.eLife · 2025Article
- Benchmarking Molecular Mutation Operators for Evolutionary Drug Design.International journal of molecular sciences · 2025Article
- Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd).Communications chemistry · 2025Article
- Assay2Mol: Large Language Model-based Drug Design Using BioAssay Context.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- A Review of Global Optimization Methods for Molecular Structures: Algorithms, Applications and Perspectives.Journal of computational chemistry · 2025Review
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- Revealing Molecule-Internal Mechanisms that Control Phonon Heat Transport through Single-Molecule Junctions by a Genetic Algorithm.ACS nano · 2025Article
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9 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.
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