DeltaDelta neural networks for lead optimization of small molecule potency

• Published in: Chemical science (Cambridge) Vol. 1; no. 47; pp. 1911 - 1918
• Main Authors: Jiménez-Luna, José, Pérez-Benito, Laura, Martínez-Rosell, Gerard, Sciabola, Simone, Torella, Rubben, Tresadern, Gary, De Fabritiis, Gianni
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 21-12-2019
• Subjects: Artificial neural networks; Computational chemistry; Computer simulation; Distance learning; Machine learning; Neural networks; Optimization; Organic chemistry
• ISSN: 2041-6520; 2041-6539
• DOI: 10.1039/c9sc04606b

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.
Machine learning approach tailored for ranking congeneric series based on 3D-convolutional neural networks tested it on over 3246 ligands and 13 targets.