scholarly journals BOKEI: Bayesian Optimization Using Knowledge of Correlated Torsions and Expected Improvement for Conformer Generation​

2019 ◽  
Author(s):  
Lucian Chan ◽  
Geoffrey Hutchison ◽  
Garrett Morris
Keyword(s):  
Random Search ◽  
Search Space ◽  
Empirical Method ◽  
Fixed Number ◽  
Low Energy ◽  
Intramolecular Interactions ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Expected Improvement ◽  
Wide Range

<div>A key challenge in conformer sampling is to find low-energy conformations with a small number of energy evaluations. We have recently demonstrated Bayesian optimization as an effective method to search for energetically favorable conformations. This approach balances between <i>exploitation</i> and <i>exploration</i>, and lead to superior performance when compared to exhaustive or random search methods. In this work, we extend strategies on proteins and oligopeptides (e.g. Ramachandran plots of secondary structure) to study the correlated torsions in small molecules. We use a bivariate von Mises distribution to capture the correlations, and use it to constrain the search space. We validate the performance of our Bayesian optimization with prior knowledge (BOKEI) on a dataset consisting of 533 diverse small organic molecules, using a force field (MMFF94) and a semi empirical method (GFN2). We compare BOKEI with Bayesian optimization with expected improvement (BOA-EI), and a genetic algorithm (GA), using a fixed number of energy evaluations. In 70(± 2.1)% of the cases examined, BOKEI finds lower energy conformations than global optimization with BOA-EI or GA. More importantly, these patterns find correlated torsions in 10-15% of molecules in larger data sets, 3-8 times more frequently than previous work. We also find that the BOKEI patterns not only describe steric clashes, but also reflect favorable intramolecular interactions, including hydrogen bonds and π-π stacking. Further understanding of the conformational preferences of molecules will help find low energy conformers efficiently for a wide range of computational modeling applications.</div>

scholarly journals BOKEI: Bayesian Optimization Using Knowledge of Correlated Torsions and Expected Improvement for Conformer Generation​

2019 ◽  
Author(s):  
Lucian Chan ◽  
Geoffrey Hutchison ◽  
Garrett Morris
Keyword(s):  
Random Search ◽  
Search Space ◽  
Empirical Method ◽  
Fixed Number ◽  
Low Energy ◽  
Intramolecular Interactions ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Expected Improvement ◽  
Wide Range

<div>A key challenge in conformer sampling is to find low-energy conformations with a small number of energy evaluations. We have recently demonstrated Bayesian optimization as an effective method to search for energetically favorable conformations. This approach balances between <i>exploitation</i> and <i>exploration</i>, and lead to superior performance when compared to exhaustive or random search methods. In this work, we extend strategies on proteins and oligopeptides (e.g. Ramachandran plots of secondary structure) to study the correlated torsions in small molecules. We use a bivariate von Mises distribution to capture the correlations, and use it to constrain the search space. We validate the performance of our Bayesian optimization with prior knowledge (BOKEI) on a dataset consisting of 533 diverse small organic molecules, using a force field (MMFF94) and a semi empirical method (GFN2). We compare BOKEI with Bayesian optimization with expected improvement (BOA-EI), and a genetic algorithm (GA), using a fixed number of energy evaluations. In 70(± 2.1)% of the cases examined, BOKEI finds lower energy conformations than global optimization with BOA-EI or GA. More importantly, these patterns find correlated torsions in 10-15% of molecules in larger data sets, 3-8 times more frequently than previous work. We also find that the BOKEI patterns not only describe steric clashes, but also reflect favorable intramolecular interactions, including hydrogen bonds and π-π stacking. Further understanding of the conformational preferences of molecules will help find low energy conformers efficiently for a wide range of computational modeling applications.</div>

scholarly journals Bayesian Optimization for Conformer Generation

2018 ◽  
Author(s):  
Lucian Chan ◽  
Geoffrey Hutchison ◽  
Garrett Morris
Keyword(s):  
Lower Energy ◽  
Random Search ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Search Method ◽  
Low Energy ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm ◽  
Conformer Generation ◽  
Stochastic Search Method

<div><div><div><div><p>Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method using Bayesian Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify differences between the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 104 (median) conformers in its search, while BOA only requires 102 energy evaluations to find top candidates. Despite evaluating fewer conformers, for many molecules, BOA finds lower-energy conformations than an exhaustive systematic Confab search.</p></div></div></div></div>

scholarly journals Bayesian Optimization for Conformer Generation

2019 ◽  
Author(s):  
Lucian Chan ◽  
Geoffrey Hutchison ◽  
Garrett Morris
Keyword(s):  
Lower Energy ◽  
Random Search ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Search Method ◽  
Low Energy ◽  
Bayesian Optimization ◽  
Mean Square ◽  
Conformer Generation ◽  
Stochastic Search Method

<p>Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method called the Bayesian</p><p>Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify the performance of the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 10<sup>4</sup> (median) conformers in its search, while BOA only requires 10<sup>2</sup> energy evaluations to find top candidates. Despite using evaluating fewer conformers, 20 − 40% of the time BOA finds lower-energy conformations than a systematic Confab search for molecules with four or more rotatable bonds.</p>

scholarly journals Bayesian Optimization for Conformer Generation

2019 ◽  
Author(s):  
Lucian Chan ◽  
Geoffrey Hutchison ◽  
Garrett Morris
Keyword(s):  
Lower Energy ◽  
Random Search ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Search Method ◽  
Low Energy ◽  
Bayesian Optimization ◽  
Mean Square ◽  
Conformer Generation ◽  
Stochastic Search Method

<p>Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method called the Bayesian</p><p>Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify the performance of the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 10<sup>4</sup> (median) conformers in its search, while BOA only requires 10<sup>2</sup> energy evaluations to find top candidates. Despite using evaluating fewer conformers, 20 − 40% of the time BOA finds lower-energy conformations than a systematic Confab search for molecules with four or more rotatable bonds.</p>

scholarly journals Bayesian Optimization for Conformer Generation

2018 ◽  
Author(s):  
Lucian Chan ◽  
Geoffrey Hutchison ◽  
Garrett Morris
Keyword(s):  
Lower Energy ◽  
Random Search ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Search Method ◽  
Low Energy ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm ◽  
Conformer Generation ◽  
Stochastic Search Method

<div><div><div><div><p>Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method using Bayesian Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify differences between the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 104 (median) conformers in its search, while BOA only requires 102 energy evaluations to find top candidates. Despite evaluating fewer conformers, for many molecules, BOA finds lower-energy conformations than an exhaustive systematic Confab search.</p></div></div></div></div>

scholarly journals BOKEI: Bayesian optimization using knowledge of correlated torsions and expected improvement for conformer generation

2020 ◽  
Vol 22 (9) ◽  
pp. 5211-5219
Author(s):  
Lucian Chan ◽  
Geoffrey R. Hutchison ◽  
Garrett M. Morris
Keyword(s):  
Low Energy ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Conformer Generation ◽  
Low Energy Conformations

A key challenge in conformer sampling is finding low-energy conformations using a small number of energy evaluations. By extracting patterns of correlated torsions, we improve the efficiency of Bayesian Optimization in finding optimal conformations.

scholarly journals Bayesian Optimization for Conformer Generation

2018 ◽  
Author(s):  
Lucian Chan ◽  
Garrett Morris ◽  
Geoffrey Hutchison
Keyword(s):  
Lower Energy ◽  
Random Search ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Search Method ◽  
Low Energy ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm ◽  
Conformer Generation ◽  
Stochastic Search Method

<div><div><div><div><p>Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method using Bayesian Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify differences between the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 104 (median) conformers in its search, while BOA only requires 102 energy evaluations to find top candidates. Despite evaluating fewer conformers, for many molecules, BOA finds lower-energy conformations than an exhaustive systematic Confab search.</p></div></div></div></div>

Understanding Conformational Entropy in Small Molecules

2020 ◽  
Author(s):  
Lucian Chan ◽  
Garrett Morris ◽  
Geoffrey Hutchison
Keyword(s):  
Small Molecules ◽  
Degrees Of Freedom ◽  
Absolute Error ◽  
Low Energy ◽  
Standard Entropy ◽  
Free Energies ◽  
Conformational Entropy ◽  
Wide Range ◽  
High Degree ◽  
Empirical Corrections

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy.

scholarly journals Low-Energy Electron Damage to Condensed-Phase DNA and Its Constituents

2021 ◽  
Vol 22 (15) ◽  
pp. 7879
Author(s):  
Yingxia Gao ◽  
Yi Zheng ◽  
Léon Sanche
Keyword(s):  
Condensed Phase ◽  
Genetic Material ◽  
High Energy ◽  
Dna Lesions ◽  
Low Energy ◽  
Experimental Investigations ◽  
Experimental Conditions ◽  
Strand Breaks ◽  
Sequence Of Events ◽  
Wide Range

The complex physical and chemical reactions between the large number of low-energy (0–30 eV) electrons (LEEs) released by high energy radiation interacting with genetic material can lead to the formation of various DNA lesions such as crosslinks, single strand breaks, base modifications, and cleavage, as well as double strand breaks and other cluster damages. When crosslinks and cluster damages cannot be repaired by the cell, they can cause genetic loss of information, mutations, apoptosis, and promote genomic instability. Through the efforts of many research groups in the past two decades, the study of the interaction between LEEs and DNA under different experimental conditions has unveiled some of the main mechanisms responsible for these damages. In the present review, we focus on experimental investigations in the condensed phase that range from fundamental DNA constituents to oligonucleotides, synthetic duplex DNA, and bacterial (i.e., plasmid) DNA. These targets were irradiated either with LEEs from a monoenergetic-electron or photoelectron source, as sub-monolayer, monolayer, or multilayer films and within clusters or water solutions. Each type of experiment is briefly described, and the observed DNA damages are reported, along with the proposed mechanisms. Defining the role of LEEs within the sequence of events leading to radiobiological lesions contributes to our understanding of the action of radiation on living organisms, over a wide range of initial radiation energies. Applications of the interaction of LEEs with DNA to radiotherapy are briefly summarized.

scholarly journals Low-energy optical switching of SO2 linkage isomerisation in single crystals of a ruthenium-based coordination complex

RSC Advances ◽  
2021 ◽  
Vol 11 (22) ◽  
pp. 13183-13192
Author(s):  
Jacqueline M. Cole ◽  
David J. Gosztola ◽  
Sven O. Sylvester
Keyword(s):  
Single Crystals ◽  
Optical Sensors ◽  
Optical Switching ◽  
Optical Switches ◽  
Low Energy ◽  
Coordination Complex ◽  
Wide Range

Single crystals that behave as optical switches are desirable for a wide range of applications, from optical sensors to read–write memory media.

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