Different molecular enumeration influences in deep learning: An example using aqueous solubility
Journal
Briefings in Bioinformatics
Journal Volume
22
Journal Issue
3
Date Issued
2021
Author(s)
Chen J.-H
Abstract
Aqueous solubility is the key property driving many chemical and biological phenomena and impacts experimental and computational attempts to assess those phenomena. Accurate prediction of solubility is essential and challenging, even with modern computational algorithms. Fingerprint-based, feature-based and molecular graph-based representations have all been used with different deep learning methods for aqueous solubility prediction. It has been clearly demonstrated that different molecular representations impact the model prediction and explainability. In this work, we reviewed different representations and also focused on using graph and line notations for modeling. In general, one canonical chemical structure is used to represent one molecule when computing its properties. We carefully examined the commonly used simplified molecular-input line-entry specification (SMILES) notation representing a single molecule and proposed to use the full enumerations in SMILES to achieve better accuracy. A convolutional neural network (CNN) was used. The full enumeration of SMILES can improve the presentation of a molecule and describe the molecule with all possible angles. This CNN model can be very robust when dealing with large datasets since no additional explicit chemistry knowledge is necessary to predict the solubility. Also, traditionally it is hard to use a neural network to explain the contribution of chemical substructures to a single property. We demonstrated the use of attention in the decoding network to detect the part of a molecule that is relevant to solubility, which can be used to explain the contribution from the CNN. ? 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Subjects
biological sciences
cheminformatics
drug discovery
medicinal chemistry
article
attention
conformation
convolutional neural network
deep learning
drug solubility
prediction
water solubility
algorithm
chemistry
solubility
water
Algorithms
Deep Learning
Neural Networks, Computer
Solubility
Water
SDGs
Type
journal article