Evaluating fast methods for static polarizabilities on extended conjugated oligomers

Hiener, Danielle; Folmsbee, Dakota; Langkamp, Luke; Hutchison, Geoffrey

doi:10.1039/d2cp02375j

Cited by 2 publications

(2 citation statements)

References 57 publications

(95 reference statements)

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“…This supports previous findings in our group that homopolymeric sequences tend to increase overall molecular polarizability (near homopolymer sequences occur often simply because they are statistically more likely to occur and have extremely similar actual polarizabilities compared to true homopolymers). 61 While a more in-depth analysis of the chemical motifs found in our realistic GA trial is beyond the scope of this work, our preliminary findings support the utility of implementing our GA best practices when conducting searches for a range of different optimized chemical properties.…”

Section: Realistic Trial: Larger Search Spacesupporting

confidence: 52%

Determining best practices for using genetic algorithms in molecular discovery

Greenstein,

Elsey,

Hutchison

2023

The Journal of Chemical Physics

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Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. We show that using a self-termination method with a minimum Spearman’s rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

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Section: Realistic Trial: Larger Search Spacesupporting

confidence: 52%

Determining best practices for using genetic algorithms in molecular discovery

Greenstein,

Elsey,

Hutchison

2023

The Journal of Chemical Physics

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“…78 Their work presents kallisto, a program that employs equations parameterized to GFN2-xTB data to compute these features. While corrections to GFN2-xTB atomic polarizabilities have been noted, 79 this approach offers the potential to significantly accelerate feature calculation to the level of GFN2-xTB computational efficiency, without directly utilizing the GFN2-xTB method.…”

Section: Future Directionsmentioning

confidence: 99%

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pK_a Predictions

Abarbanel,

Hutchison

2024

J. Chem. Theory Comput.

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Accurate prediction of micro-pK a values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pK a prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pK a units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pK a prediction models. QupKake represents a significant advancement in micro-pK a prediction, offering a powerful tool for various applications in chemistry and beyond.

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Evaluating fast methods for static polarizabilities on extended conjugated oligomers

Abstract: Given the importance of accurate polarizability calculations to many chemical applications, coupled with the need for efficiency when calculating the properties of sets of molecules or large oligomers, we present...

Cited by 2 publications

References 57 publications

Determining best practices for using genetic algorithms in molecular discovery

Determining best practices for using genetic algorithms in molecular discovery

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pK_a Predictions

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Evaluating fast methods for static polarizabilities on extended conjugated oligomers

Abstract: Given the importance of accurate polarizability calculations to many chemical applications, coupled with the need for efficiency when calculating the properties of sets of molecules or large oligomers, we present...

Cited by 2 publications

References 57 publications

Determining best practices for using genetic algorithms in molecular discovery

Determining best practices for using genetic algorithms in molecular discovery

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions

Contact Info

Product

Resources

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QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pK_a Predictions