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 ◽  
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>

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 Black-box combinatorial optimization using models with integer-valued minima

2020 ◽  
Author(s):  
Laurens Bliek ◽  
Sicco Verwer ◽  
Mathijs de Weerdt
Keyword(s):  
Optimization Algorithm ◽  
Surrogate Model ◽  
Optimization Problems ◽  
Random Search ◽  
Continuous Optimization ◽  
Optimal Solution ◽  
Black Box ◽  
Combinatorial Problems ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm

Abstract When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables.

A novel stochastic search method for polygonal approximation problem

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3216-3223 ◽  
Author(s):  
Bin Wang ◽  
Huazhong Shu ◽  
Chaojian Shi ◽  
Limin Luo
Keyword(s):  
Approximation Problem ◽  
Stochastic Search ◽  
Search Method ◽  
Polygonal Approximation ◽  
Stochastic Search Method

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>

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