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 ◽  
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 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

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 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>

Gaussian Process Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling

2021 ◽  
Author(s):  
Arunabha Batabyal ◽  
Sugrim Sagar ◽  
Jian Zhang ◽  
Tejesh Dube ◽  
Xuehui Yang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Phase Field ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Neck Size ◽  
Sources Of Uncertainty ◽  
Surface Diffusivity ◽  
Input Parameters ◽  
Optimal Values ◽  
Probability Of Improvement

Abstract A persistent problem in the selective laser sintering process is to maintain the quality of additively manufactured parts, which can be attributed to the various sources of uncertainty. In this work, a two-particle phase-field microstructure model has been analyzed. The sources of uncertainty as the two input parameters were surface diffusivity and inter-particle distance. The response quantity of interest (QOI) was selected as the size of the neck region that develops between the two particles. Two different cases with equal and unequal sized particles were studied. It was observed that the neck size increased with increasing surface diffusivity and decreased with increasing inter-particle distance irrespective of particle size. Sensitivity analysis found that the inter-particle distance has more influence on variation in neck size than that of surface diffusivity. The machine learning algorithm Gaussian Process Regression was used to create the surrogate model of the QOI. Bayesian Optimization method was used to find optimal values of the input parameters. For equal-sized particles, optimization using Probability of Improvement provided optimal values of surface diffusivity and inter-particle distance as 23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition function gave optimal values 23.9874 and 40.7428, respectively. For unequal sized particles, optimal design values from Probability of Improvement were 23.9700 and 33.3005, respectively, while those from Expected Improvement were 23.9893 and 33.9627, respectively. The optimization results from the two different acquisition functions seemed to be in good agreement.

Spatial Structure of the Acth-(6-9)-PGP Molecule

2021 ◽  
Vol 43 (5) ◽  
pp. 500-500
Author(s):  
Namiq Akhmedov Namiq Akhmedov ◽  
Leyla Agayeva Leyla Agayeva ◽  
Gulnara Akverdieva Gulnara Akverdieva ◽  
Rena Abbasli and Larisa Ismailova Rena Abbasli and Larisa Ismailova
Keyword(s):  
Amino Acid ◽  
Spatial Structure ◽  
Main Chain ◽  
Low Energy ◽  
Dihedral Angles ◽  
Amino Acid Residues ◽  
Analysis Method ◽  
Theoretical Conformational Analysis ◽  
Energy Of Hydrogen Bonds ◽  
Low Energy Conformations

The spatial structure of ACTH-(6-9)-PGP molecule has been investigated using theoretical conformational analysis method. Amino acid sequence of the N-terminal pentapeptide fragment of His-Phe-Arg-Trp-Pro of this molecule conforms to the fragment 6-9 of ACTH hormone. Calculations of conformational states of this molecule are carried out regarding nonvalent, electrostatic and torsional interactions and the energy of hydrogen bonds. The spatial structure of the His-Phe-Arg-Trp-Pro-Gly-Pro molecule was estimated on the low–energy conformations of the N-terminal tetrapeptide fragment His-Phe-Arg-Trp and C-terminal tripeptide fragment Pro-Gly-Pro of this molecule. It is shown that the spatial structure of heptapeptide molecule can be presented by 11 low-energy forms of the main chain. The low–energy conformations of this molecule, the values of dihedral angles of the backbone and side chains of the amino acid residues were founded and the energies of intra- and inter-residual interactions were determined.

Sign in / Sign up

Export Citation Format

Share Document