AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

Jacob O. Spiegel, Jacob D. Durrant

Journal of Cheminformatics 2020 article cited by 171

Source Open-access PDF More in Biology & Life Sciences

importance 4/10 ai assisted Computational Drug Discovery Methods drug discovery stage: research open-source model

Problem

Generating novel drug-like molecules and optimizing existing lead compounds is slow and costly in drug discovery.

Why it matters

Computational design can accelerate the early, expensive stages of drug discovery.

What the AI did

AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand for de novo design and lead optimization, applied to PARP-1.

Results

Drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors; predictions only, no experimental validation stated.

Impact

Provides an open-source design tool; impact limited to computational docking predictions.

Record

Venue	Journal of Cheminformatics
Published	2020
Work type	article
Authors	Jacob O. Spiegel, Jacob D. Durrant
Citations	171 (OpenAlex, at import time)
References	112
Model or method	AutoGrow4 genetic algorithm
Organization	not stated in source
Primary source	https://doi.org/10.1186/s13321-020-00429-4
Open access	gold
Keywords	genetic algorithm, de novo drug design, lead optimization, docking, PARP-1

Importance rating

4 / 10

real but narrower advance

Open-source de novo drug design tool; abstract gives no scale of impact and only computational docking predictions on one target (PARP-1), with no experimental validation stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Biology & Life Sciences

#	Paper	Year	Citations	Importance
1	AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models Mihály Váradi, Stephen Anyango, Mandar Deshpande, Sreenath Sasidharan...	2021	7,782	9
2	Evolutionary-scale prediction of atomic-level protein structure with a language model Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting L...	2023	5,455	9
3	Accurate prediction of protein structures and interactions using a three-track neural network Minkyung Baek, Frank DiMaio, Ivan V. Anishchenko, Justas Dauparas, Ser...	2021	5,468	9
4	De novo design of protein structure and function with RFdiffusion Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Tripp...	2023	2,294	9
5	Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins Rebecca Buller, Jiřı́ Damborský, Donald Hilvert, Uwe T. Bornscheuer	2024	40	9