ArticleJournal of molecular graphics & modelling2013
AutoGrow 3.0: an improved algorithm for chemically tractable, semi-automated protein inhibitor design.
Article in Journal of molecular graphics & modelling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- 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
- 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
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- Computer-aided multi-objective optimization in small molecule discovery.Patterns (New York, N.Y.) · 2023Review
- Introducing ligand GA, a genetic algorithm molecular tool for automated protein inhibitor design.Scientific reports · 2022Article
- A molecular evolution algorithm for ligand design in DOCK.Journal of computational chemistry · 2022Article
- MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction.ACS omega · 2022Article
- DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization.Journal of chemical information and modeling · 2021Article
- An up-to-date overview of computational polypharmacology in modern drug discovery.Expert opinion on drug discovery · 2020Review
- AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization.Journal of cheminformatics · 2020Article
- Review
- In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions.Molecules (Basel, Switzerland) · 2019Article
- Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets.Journal of computational chemistry · 2017Article
- Scoria: a Python module for manipulating 3D molecular data.Journal of cheminformatics · 2017Article
- Computational polypharmacology: a new paradigm for drug discovery.Expert opinion on drug discovery · 2017Review
- Computational identification of novel entry inhibitor scaffolds mimicking primary receptor CD4 of HIV-1 gp120.Journal of molecular modeling · 2017Article
- Open source molecular modeling.Journal of molecular graphics & modelling · 2016Review
- Article
- Computational methods in drug discovery.Beilstein journal of organic chemistry · 2016Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We here present an improved version of AutoGrow (version 3.0), an evolutionary algorithm that works in conjunction with existing open-source software to automatically optimize candidate ligands for predicted binding affinity and other druglike properties. Though no substitute for the medicinal chemist, AutoGrow 3.0, unlike its predecessors, attempts to introduce some chemical intuition into the automated optimization process. AutoGrow 3.0 uses the rules of click chemistry to guide optimization, greatly enhancing synthesizability. Additionally, the program discards any growing ligand whose physical and chemical properties are not druglike. By carefully crafting chemically feasible druglike molecules, we hope that AutoGrow 3.0 will help supplement the chemist's efforts. To demonstrate the utility of the program, we use AutoGrow 3.0 to generate predicted inhibitors of three important drug targets: Trypanosoma brucei RNA editing ligase 1, peroxisome proliferator-activated receptor γ, and dihydrofolate reductase. In all cases, AutoGrow generates druglike molecules with high predicted binding affinities. AutoGrow 3.0 is available free of charge (http://autogrow.ucsd.edu) under the terms of the GNU General Public License and has been tested on Linux and Mac OS X.
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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.