Predicting bioaccumulation using molecular theory: A machine learning approach

TL;DR

This paper introduces a novel machine learning method combining molecular theory and 3D convolutional neural networks to predict bioaccumulation factors of organic molecules with high accuracy.

Contribution

The study presents a new approach integrating 3D-RISM molecular theory with neural networks for bioaccumulation prediction, advancing property prediction methods.

Findings

Achieved high prediction accuracy for bioaccumulation factors.

Demonstrated effectiveness of combining molecular theories with machine learning.

Showed potential for predicting properties inaccessible to physics-based models.

Abstract

In this work, we present a new method for predicting bioaccumulation factor of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These spatial distributions are obtained by a molecular theory called three-dimensional reference interaction site model (3D-RISM). We have shown that the method allows one to achieve a good accuracy of prediction. Our research demonstrates that combination of molecular theories with modern machine learning approaches can be effectively used for predicting properties that are otherwise inaccessible to purely physics-based models.