scholarly journals Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-like Molecules

2019 ◽  
Author(s):  
Yashaswi Pathak ◽  
Siddhartha Laghuvarapu ◽  
Sarvesh Mehta ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Free Energy ◽  
Message Passing ◽  
Solvation Free Energy ◽  
The Body ◽  
Free Energies ◽  
Pharmacokinetic Properties ◽  
Interaction Map ◽  
Solvent Molecules ◽  
Solvation Free Energies

<div>Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents within the limits of chemical accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.</div>

scholarly journals Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-Like Molecules

2020 ◽  
Vol 34 (01) ◽  
pp. 873-880 ◽  
Author(s):  
Yashaswi Pathak ◽  
Siddhartha Laghuvarapu ◽  
Sarvesh Mehta ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Free Energy ◽  
Message Passing ◽  
Solvation Free Energy ◽  
The Body ◽  
Free Energies ◽  
Pharmacokinetic Properties ◽  
Interaction Map ◽  
Solvent Molecules ◽  
Solvation Free Energies

Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents with high accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.

scholarly journals Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-like Molecules

2019 ◽  
Author(s):  
Yashaswi Pathak ◽  
Siddhartha Laghuvarapu ◽  
Sarvesh Mehta ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Free Energy ◽  
Message Passing ◽  
Solvation Free Energy ◽  
The Body ◽  
Free Energies ◽  
Pharmacokinetic Properties ◽  
Interaction Map ◽  
Solvent Molecules ◽  
Solvation Free Energies

<div>Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents within the limits of chemical accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.</div>

scholarly journals Chemically Interpretable Graph Interaction Network for Prediction of Pharmacokinetic Properties of Drug-like Molecules

2019 ◽  
Author(s):  
Yashaswi Pathak ◽  
Siddhartha Laghuvarapu ◽  
Sarvesh Mehta ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Free Energy ◽  
Message Passing ◽  
Solvation Free Energy ◽  
The Body ◽  
Free Energies ◽  
Pharmacokinetic Properties ◽  
Interaction Map ◽  
Solvent Molecules ◽  
Solvation Free Energies

<div>Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents within the limits of chemical accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.</div>

scholarly journals Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Amin Alibakhshi ◽  
Bernd Hartke
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Solvation Free Energy ◽  
Continuum Solvation ◽  
Free Energies ◽  
Solvation Model ◽  
Continuum Solvation Model ◽  
Solvation Models ◽  
Solvation Free Energies ◽  
Polarizable Continuum

AbstractTheoretical estimation of solvation free energy by continuum solvation models, as a standard approach in computational chemistry, is extensively applied by a broad range of scientific disciplines. Nevertheless, the current widely accepted solvation models are either inaccurate in reproducing experimentally determined solvation free energies or require a number of macroscopic observables which are not always readily available. In the present study, we develop and introduce the Machine-Learning Polarizable Continuum solvation Model (ML-PCM) for a substantial improvement of the predictability of solvation free energy. The performance and reliability of the developed models are validated through a rigorous and demanding validation procedure. The ML-PCM models developed in the present study improve the accuracy of widely accepted continuum solvation models by almost one order of magnitude with almost no additional computational costs. A freely available software is developed and provided for a straightforward implementation of the new approach.

scholarly journals Use of scaled-particle theory in the assessment of the Ph4As+/Ph4B− assumption for single ions

1979 ◽  
Vol 57 (1) ◽  
pp. 71-76 ◽  
Author(s):  
Michael H. Abraham ◽  
Asadollah Nasehzadeh
Keyword(s):  
Free Energy ◽  
Recent Work ◽  
Scaled Particle Theory ◽  
Cavity Formation ◽  
Energy Term ◽  
Free Energies ◽  
Particle Theory ◽  
Novel Method ◽  
Energy Of Interaction ◽  
Solvent Molecules

A novel method for the assessment of the Ph4As+/Ph4B− assumption for free energies of transfer of single ions has recently been suggested by Treiner, and used by him to deduce that the assumption is not valid for transfers between water, propylene carbonate, sulpholane, dimethylsulphoxide, N-methyl-2-pyrrolidone, and perhaps also dimethylformamide. The basis of the method is the estimation of the free energy of cavity formation by scaled-particle theory, together with the hypothesis that the free energy of interaction of Ph4As+ (or Ph4B−) with solvent molecules is the same in all solvents, ΔGt0(int) = 0. It is shown in the present paper that (a) whether or not the Ph4As+/Ph4B− assumption applies to transfer to a given solvent depends on which other solvent is taken as the reference solvent in Treiner's method, (b) the calculation of the cavity free energy term by scaled-particle theory and by the theory of Sinanoglu – Reisse – Moura Ramos (SRMR) yields values so different that the method cannot be considered reliable, (c) the calculation of cavity enthalpies and entropies for Ph4As+ or Ph4B− by scaled-particle theory yields results that are chemically not reasonable, (d) the hypothesis that ΔGt0(int) = 0 conflicts with SRMR theory, and (e) the conclusions reached by Treiner are not in accord with recent work that in general supports the Ph4As+/Ph4B− assumption for solvents that are rejected by Treiner.

scholarly journals Machine Learning Guided Approach for Studying Solvation Environments

2019 ◽  
Author(s):  
Yasemin Basdogan ◽  
Mitchell C. Groenenboom ◽  
Ethan Henderson ◽  
Sandip De ◽  
Susan Rempe ◽  
...  
Keyword(s):  
A Priori ◽  
Optimization Procedure ◽  
Low Energy ◽  
Free Energies ◽  
Continuum Approach ◽  
Explicit Solvent ◽  
Linear Dimensionality Reduction ◽  
Solvent Molecules ◽  
Dynamics Simulations ◽  
Solvation Free Energies

<div><div><div><p>Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum mechanics based cluster-continuum approach for calculating single ion solvation free energies. This approach uses a global optimization procedure to identify low energy molecular clusters with different numbers of explicit solvent molecules and then employs the Smooth Overlap for Atomic Positions (SOAP) kernel to quantify the similarity between different low energy solute environments. From these data, we use sketch-map, a non-linear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies with errors within five percent of experimental measurements for most cases.</p></div></div></div>

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