scholarly journals Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

2016 ◽  
Vol 30 (9) ◽  
pp. 743-751 ◽  
Author(s):  
Nanjie Deng ◽  
William F. Flynn ◽  
Junchao Xia ◽  
R. S. K. Vijayan ◽  
Baofeng Zhang ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Large Scale ◽  
Free Energy Calculations ◽  
Grand Challenge ◽  
Energy Calculations ◽  
Scale Free ◽  
D3r Grand Challenge 2015

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.

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresdern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

scholarly journals Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset

2019 ◽  
Vol 33 (12) ◽  
pp. 1031-1043 ◽  
Author(s):  
Eddy Elisée ◽  
Vytautas Gapsys ◽  
Nawel Mele ◽  
Ludovic Chaput ◽  
Edithe Selwa ◽  
...  
Keyword(s):  
Free Energy ◽  
Performance Evaluation ◽  
Molecular Docking ◽  
Free Energy Calculations ◽  
Grand Challenge ◽  
Energy Calculations

Large-Scale Free Energy Calculations on a Computational MOF Database: Toward Synthetic Likelihood Predictions

2020 ◽  
Author(s):  
Ryther Anderson ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Large Scale ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Scale Free ◽  
Computational Screening ◽  
Synthetic Accessibility ◽  
Isomorphic Series ◽  
Metal Organic

Metal-organic frameworks (MOFs) have captivated the research community due to a modular crystal structure that is tailorable for many applications. However, with millions of possible MOFs to be considered, it is challenging to identify the ideal MOF for the application of choice. Although computational screening of MOF databases has provided a fast way to evaluate MOF properties, validation experiments on predicted “exceptional” MOFs are not common due to uncertainties on the synthetic likelihood of computationally constructed MOFs, hence hindering material discovery. Aiming to leverage the perspective provided by large datasets, here we created and screened a topologically diverse database of 8,500 MOFs to interrogate whether thermodynamic stability metrics such as free energy could be used to generally predict the synthetic likelihood of computationally constructed MOFs. To this end, we first evaluated the suitability of two methods and three force fields to calculate free energies in MOFs at large scale, settling on the Frenkel-Ladd path thermodynamic integration method and the UFF4MOF force field. Upon defining a relative free energy, Δ<sub>LM</sub>F<sub>FL</sub>, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below Δ<sub>LM</sub>F<sub>FL</sub> = 4.4 kJ/mol per atom, suggesting a general first filter to discriminate between synthetically likely and unlikely MOFs. However, a second filter is needed when several MOF isomorphs are below the Δ<sub>LM</sub>F<sub>FL</sub> threshold. In 84% of the cases, the synthetically accessible MOF within an isomorphic series presented the lowest predicted free energy. The present; work suggests that crystal free energies could be key to understanding synthetic likelihood for MOFs in computational databases (and MOFs in general), and that the thermodynamics stability of the fully assembled MOF often determines synthetic accessibility.

Large-Scale Free Energy Calculations on a Computational MOF Database: Toward Synthetic Likelihood Predictions

2020 ◽  
Author(s):  
Ryther Anderson ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Large Scale ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Scale Free ◽  
Computational Screening ◽  
Synthetic Accessibility ◽  
Isomorphic Series ◽  
Metal Organic

Metal-organic frameworks (MOFs) have captivated the research community due to a modular crystal structure that is tailorable for many applications. However, with millions of possible MOFs to be considered, it is challenging to identify the ideal MOF for the application of choice. Although computational screening of MOF databases has provided a fast way to evaluate MOF properties, validation experiments on predicted “exceptional” MOFs are not common due to uncertainties on the synthetic likelihood of computationally constructed MOFs, hence hindering material discovery. Aiming to leverage the perspective provided by large datasets, here we created and screened a topologically diverse database of 8,500 MOFs to interrogate whether thermodynamic stability metrics such as free energy could be used to generally predict the synthetic likelihood of computationally constructed MOFs. To this end, we first evaluated the suitability of two methods and three force fields to calculate free energies in MOFs at large scale, settling on the Frenkel-Ladd path thermodynamic integration method and the UFF4MOF force field. Upon defining a relative free energy, Δ<sub>LM</sub>F<sub>FL</sub>, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below Δ<sub>LM</sub>F<sub>FL</sub> = 4.4 kJ/mol per atom, suggesting a general first filter to discriminate between synthetically likely and unlikely MOFs. However, a second filter is needed when several MOF isomorphs are below the Δ<sub>LM</sub>F<sub>FL</sub> threshold. In 84% of the cases, the synthetically accessible MOF within an isomorphic series presented the lowest predicted free energy. The present; work suggests that crystal free energies could be key to understanding synthetic likelihood for MOFs in computational databases (and MOFs in general), and that the thermodynamics stability of the fully assembled MOF often determines synthetic accessibility.

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