Methods To Improve the Calculations of Solvation Model Density Solvation Free Energies and Associated Aqueous pKa Values: Comparison between Choosing an Optimal Theoretical Level, Solute Cavity Scaling, and Using Explicit Solvent Molecules

2019 ◽  
Vol 123 (34) ◽  
pp. 7430-7438 ◽  
Author(s):  
Longkun Xu ◽  
Michelle L. Coote
Keyword(s):  
Free Energies ◽  
Theoretical Level ◽  
Solvation Model ◽  
Pka Values ◽  
Explicit Solvent ◽  
Aqueous Pka ◽  
Solvent Molecules ◽  
Solvation Free Energies

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>

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>

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>

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