The role of human in the loop: lessons from D3R challenge 4.

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ID: 91354
2020
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Abstract
The rapid development of new machine learning techniques led to significant progress in the area of computer-aided drug design. However, despite the enormous predictive power of new methods, they lack explainability and are often used as black boxes. The most important decisions in drug discovery are still made by human experts who rely on intuitions and simplified representation of the field. We used D3R Grand Challenge 4 to model contributions of human experts during the prediction of the structure of protein-ligand complexes, and prediction of binding affinities for series of ligands in the context of absence or abundance of experimental data. We demonstrated that human decisions have a series of biases: a tendency to focus on easily identifiable protein-ligand interactions such as hydrogen bonds, and neglect for a more distributed and complex electrostatic interactions and solvation effects. While these biases still allow human experts to compete with blind algorithms in some areas, the underutilization of the information leads to significantly worse performance in data-rich tasks such as binding affinity prediction.
Reference Key
stroganov2020thejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Stroganov, Oleg V;Novikov, Fedor N;Medvedev, Michael G;Dmitrienko, Artem O;Gerasimov, Igor;Svitanko, Igor V;Chilov, Ghermes G;
Journal journal of computer-aided molecular design
Year 2020
DOI
10.1007/s10822-020-00291-4
URL
Keywords
Machine learning molecular docking d3r drug design data resource human in the loop lead finder

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