An Asymmetric Mechanism in a Symmetric Molecular Machine

10.26434/chemrxiv.12053247.v1 ◽

2020 ◽

Author(s):

Florian Blanc ◽

Marco Cecchini

Keyword(s):

Free Energy ◽

Large Scale ◽

Technological Development ◽

Thermal Fluctuations ◽

Free Energy Calculations ◽

Molecular Machine ◽

Free Energy Surface ◽

Promising Area ◽

Sequential Mechanism ◽

External Stimuli

The design of molecular architectures exhibiting functional motions is a promising area for disruptive technological development. Towards this goal, rotaxanes and catenanes, which undergo relative motions of their sub-units in response to external stimuli, are prime candidates. Here, we report on the computational analysis of the contraction/extension of a bistable [c2]-daisy chain rotaxane. Using free energy calculations and transition path optimizations, we explore the free energy landscape governing the functional motions of a prototypical molecular machine with atomic resolution. The calculations reveal a sequential mechanism for contraction/extension in which the asynchronous gliding of each ring is preferred over the concerted movement suggested by chemical intuition. Analysis of the underlying free energy surface indicates that dissymmetric gliding is favored because it entails crossings of much smaller barriers. Our findings illustrate an important design principle for molecular machines, namely that efficient exploitation of thermal fluctuations may be realized by breaking down the large-scale functional motions into smaller steps.

Download Full-text

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

10.26434/chemrxiv.11364884.v2 ◽

2020 ◽

Cited By ~ 3

Author(s):

Christina Schindler ◽

Hannah Baumann ◽

Andreas Blum ◽

Dietrich Böse ◽

Hans-Peter Buchstaller ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Large Scale ◽

Active Drug ◽

Binding Free Energy ◽

Free Energy Calculations ◽

Energy Calculation ◽

Large Scale Assessment ◽

New Public ◽

Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.

Download Full-text

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

10.26434/chemrxiv.7841270.v2 ◽

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 IC50 < 100 nM, and four unique cores with a predicted IC50 < 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.

Download Full-text

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

10.26434/chemrxiv.7841270 ◽

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 IC50 < 100 nM, and four unique cores with a predicted IC50 < 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.

Download Full-text

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

10.26434/chemrxiv.11874816.v1 ◽

2020 ◽

Cited By ~ 1

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, ΔLMFFL, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below ΔLMFFL = 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 ΔLMFFL 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.

Download Full-text

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

10.26434/chemrxiv.11874816 ◽

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, ΔLMFFL, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below ΔLMFFL = 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 ΔLMFFL 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.

Download Full-text

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

10.26434/chemrxiv.11364884 ◽

2020 ◽

Cited By ~ 2

Author(s):

Christina Schindler ◽

Hannah Baumann ◽

Andreas Blum ◽

Dietrich Böse ◽

Hans-Peter Buchstaller ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Large Scale ◽

Active Drug ◽

Binding Free Energy ◽

Free Energy Calculations ◽

Energy Calculation ◽

Large Scale Assessment ◽

New Public ◽

Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.

Download Full-text

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

10.26434/chemrxiv.7841270.v3 ◽

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 IC50 < 100 nM, and four unique cores with a predicted IC50 < 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.

Download Full-text