scholarly journals Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 686 ◽  
Author(s):  
Guilherme Duarte Ramos Matos ◽  
David L. Mobley
Keyword(s):  
Free Energy ◽  
Chemical Potential ◽  
Computational Cost ◽  
Energy Model ◽  
Free Energy Calculations ◽  
Limiting Factor ◽  
Solubility Prediction ◽  
Energy Calculations ◽  
Chemical Potentials ◽  
The Absolute

Background: Solubility is a physical property of high importance to the pharmaceutical industry, the prediction of which for potential drugs has so far been a hard task. We attempted to predict the solubility of acetylsalicylic acid (ASA) by estimating the absolute chemical potentials of its most stable polymorph and of solutions with different concentrations of the drug molecule. Methods: Chemical potentials were estimated from all-atom molecular dynamics simulations.  We used the Einstein molecule method (EMM) to predict the absolute chemical potential of the solid and solvation free energy calculations to predict the excess chemical potentials of the liquid-phase systems. Results: Reliable estimations of the chemical potentials for the solid and for a single ASA molecule using the EMM required an extremely large number of intermediate states for the free energy calculations, meaning that the calculations were extremely demanding computationally. Despite the computational cost, however, the computed value did not agree well with the experimental value, potentially due to limitations with the underlying energy model. Perhaps better values could be obtained with a better energy model; however, it seems likely computational cost may remain a limiting factor for use of this particular approach to solubility estimation.    Conclusions: Solubility prediction of drug-like solids remains computationally challenging, and it appears that both the underlying energy model and the computational approach applied may need improvement before the approach is suitable for routine use.

scholarly journals Challenges in the use of atomistic simulations to predict solubilities of drug-like molecules

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 686 ◽  
Author(s):  
Guilherme Duarte Ramos Matos ◽  
David L. Mobley
Keyword(s):  
Free Energy ◽  
Chemical Potential ◽  
Computational Cost ◽  
Energy Model ◽  
Free Energy Calculations ◽  
Limiting Factor ◽  
Solubility Prediction ◽  
Energy Calculations ◽  
Chemical Potentials ◽  
The Absolute

Background: Solubility is a physical property of high importance to the pharmaceutical industry, the prediction of which for potential drugs has so far been a hard task. We attempted to predict the solubility of acetylsalicylic acid (ASA) by estimating the absolute chemical potentials of its most stable polymorph and of solutions with different concentrations of the drug molecule. Methods: Chemical potentials were estimated from all-atom molecular dynamics simulations.  We used the Einstein molecule method (EMM) to predict the absolute chemical potential of the solid and solvation free energy calculations to predict the excess chemical potentials of the liquid-phase systems. Results: Reliable estimations of the chemical potentials for the solid and for a single ASA molecule using the EMM required an extremely large number of intermediate states for the free energy calculations, meaning that the calculations were extremely demanding computationally. Despite the computational cost, however, the computed value did not agree well with the experimental value, potentially due to limitations with the underlying energy model. Perhaps better values could be obtained with a better energy model; however, it seems likely computational cost may remain a limiting factor for use of this particular approach to solubility estimation.    Conclusions: Solubility prediction of drug-like solids remains computationally challenging, and it appears that both the underlying energy model and the computational approach applied may need improvement before the approach is suitable for routine use.

scholarly journals Challenges of the Use of Atomistic Simulations to Predict Solubilities of Drug-like Molecules

2018 ◽  
Author(s):  
Guilherme Duarte Ramos Matos ◽  
David Mobley
Keyword(s):  
Free Energy ◽  
Chemical Potential ◽  
Computational Cost ◽  
Physical Property ◽  
Energy Model ◽  
Free Energy Calculations ◽  
Limiting Factor ◽  
Solubility Prediction ◽  
Energy Calculations ◽  
Chemical Potentials

<div><b>Background.</b></div><div>Solubility is a physical property of extreme importance to the Pharmaceutical industry whose prediction for potential drugs has so far been a hard task.</div><div>We attempted to predict the solubility of acetylsalicylic acid (ASA) by estimating absolute chemical potentials of its most stable polymorph and of solutions with different concentrations of the drug molecule.</div><div><br></div><div><b>Methods.</b></div><div>Chemical potentials were estimated from all-atom molecular dynamics simulations. </div><div>We used the Einstein Molecule Method to predict the absolute chemical potential of the solid and solvation free energy calculations to predict the excess chemical potentials of the liquid phase systems.</div><div><br></div><div><b>Results.</b></div><div>Reliable estimations of the chemical potentials for the solid and for a single ASA molecule using the Einstein Molecule Method required an extremely large number of intermediate states for the free energy calculations, meaning that the calculations were extremely demanding computationally.</div><div>Despite the computational cost, however, the computed value did not agree well with experiment, potentially due to limitations with the underlying energy model.</div><div>Perhaps better values could be obtained with a better energy model; however, it seems likely computational cost may remain a limiting factor for use of this particular approach to solubility estimation. </div><div><br></div><div><b>Conclusions.</b></div><div>Solubility prediction of drug-like solids still is a challenge on the computational side, and it appears that both the underlying energy model and the computational approach applied may need improvement before the approach is suitable for routine use.</div>

scholarly journals Challenges of the Use of Atomistic Simulations to Predict Solubilities of Drug-like Molecules

2018 ◽  
Author(s):  
Guilherme Duarte Ramos Matos ◽  
David Mobley
Keyword(s):  
Free Energy ◽  
Chemical Potential ◽  
Computational Cost ◽  
Physical Property ◽  
Energy Model ◽  
Free Energy Calculations ◽  
Limiting Factor ◽  
Solubility Prediction ◽  
Energy Calculations ◽  
Chemical Potentials

<div><b>Background.</b></div><div>Solubility is a physical property of extreme importance to the Pharmaceutical industry whose prediction for potential drugs has so far been a hard task.</div><div>We attempted to predict the solubility of acetylsalicylic acid (ASA) by estimating absolute chemical potentials of its most stable polymorph and of solutions with different concentrations of the drug molecule.</div><div><br></div><div><b>Methods.</b></div><div>Chemical potentials were estimated from all-atom molecular dynamics simulations. </div><div>We used the Einstein Molecule Method to predict the absolute chemical potential of the solid and solvation free energy calculations to predict the excess chemical potentials of the liquid phase systems.</div><div><br></div><div><b>Results.</b></div><div>Reliable estimations of the chemical potentials for the solid and for a single ASA molecule using the Einstein Molecule Method required an extremely large number of intermediate states for the free energy calculations, meaning that the calculations were extremely demanding computationally.</div><div>Despite the computational cost, however, the computed value did not agree well with experiment, potentially due to limitations with the underlying energy model.</div><div>Perhaps better values could be obtained with a better energy model; however, it seems likely computational cost may remain a limiting factor for use of this particular approach to solubility estimation. </div><div><br></div><div><b>Conclusions.</b></div><div>Solubility prediction of drug-like solids still is a challenge on the computational side, and it appears that both the underlying energy model and the computational approach applied may need improvement before the approach is suitable for routine use.</div>

Entropy deepens loading chemical potentials of small alcohols by narrow carbon nanotubes

2020 ◽  
Vol 22 (39) ◽  
pp. 22369-22379
Author(s):  
Mateo Barria-Urenda ◽  
Jose Antonio Garate
Keyword(s):  
Carbon Nanotubes ◽  
Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Chemical Potentials

Small alcohol confinement within narrow carbon nanotubes has been extensively and systematically studied via rigorous free-energy calculations.

scholarly journals Accurate calculation of the absolute free energy of binding for drug molecules

2016 ◽  
Vol 7 (1) ◽  
pp. 207-218 ◽  
Author(s):  
Matteo Aldeghi ◽  
Alexander Heifetz ◽  
Michael J. Bodkin ◽  
Stefan Knapp ◽  
Philip C. Biggin
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Free Energy Calculations ◽  
Accurate Calculation ◽  
Energy Calculations ◽  
Thermodynamic Cycles ◽  
Drug Molecules ◽  
The Absolute ◽  
Free Energy Of Binding

Free energy calculations based on molecular dynamics and thermodynamic cycles accurately reproduce experimental affinities of diverse bromodomain inhibitors.

Free-energy calculations using classical molecular simulation: application to the determination of the melting point and chemical potential of a flexible RDX model

2016 ◽  
Vol 18 (11) ◽  
pp. 7841-7850 ◽  
Author(s):  
Michael S. Sellers ◽  
Martin Lísal ◽  
John K. Brennan
Keyword(s):  
Free Energy ◽  
Melting Point ◽  
Molecular Simulation ◽  
Chemical Potential ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Liquid Phases

Several methods are used in sequence to determine the chemical potential of atomistic RDX in the solid and liquid phases, and its corresponding melting point. Results yield the thermodynamic melting point of 488.75 K at 1.0 atm.

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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