Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias

Kovács, Dávid Péter; McCorkindale, William; Lee, Alpha A.

doi:10.26434/chemrxiv.13061402.v1

Cited by 11 publications

(11 citation statements)

References 27 publications

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“…In contrast to our work, in SMILES-to-SMILES translations chemical changes mostly occur via rearrangements of SMILES tokens rather than actual transformations of chemically meaningful tokens, which hampers chemical interpretability and explainability. To address this issue, Kovács et al proposed a framework to interpret the results of Molecular Transformer 45 .…”

Section: Resultsmentioning

confidence: 99%

Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments

Ucak

Ashyrmamatov

Ko³

et al. 2022

Nat Commun

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Designing efficient synthetic routes for a target molecule remains a major challenge in organic synthesis. Atom environments are ideal, stand-alone, chemically meaningful building blocks providing a high-resolution molecular representation. Our approach mimics chemical reasoning, and predicts reactant candidates by learning the changes of atom environments associated with the chemical reaction. Through careful inspection of reactant candidates, we demonstrate atom environments as promising descriptors for studying reaction route prediction and discovery. Here, we present a new single-step retrosynthesis prediction method, viz. RetroTRAE, being free from all SMILES-based translation issues, yields a top-1 accuracy of 58.3% on the USPTO test dataset, and top-1 accuracy reaches to 61.6% with the inclusion of highly similar analogs, outperforming other state-of-the-art neural machine translation-based methods. Our methodology introduces a novel scheme for fragmental and topological descriptors to be used as natural inputs for retrosynthetic prediction tasks.

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Section: Resultsmentioning

confidence: 99%

Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments

Ucak

Ashyrmamatov

Ko³

et al. 2022

Nat Commun

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“…The insufficient nature of mean error metrics has been pointed out before. [37][38][39] In addition to the above data sets, we also demonstrate the use of ACE on a slightly larger, significantly more flexible molecule that is more representative of the needs of medicinal chemistry applications.…”

Section: Introductionmentioning

confidence: 84%

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

Kovács

Oord

Kucera

et al. 2021

Preprint

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We demonstrate that accurate linear force fields can be built using the Atomic Cluster Expansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural extension of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as refitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking.

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“…The insufficient nature of mean error metrics has been pointed out before. [39][40][41] In addition to the above data sets, we also demonstrate the use of ACE on a slightly larger, significantly more flexible molecule that is more representative of the needs of medicinal chemistry applications.…”

Section: Introductionmentioning

confidence: 84%

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

Kovács

Oord

Kučera

et al. 2021

Preprint

Self Cite

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We demonstrate that fast and accurate linear force fields can be built for molecules using the Atomic Cluster Expansion (ACE) framework. The ACE models parametrize the Potential Energy Surface in terms of body ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the 4 or 5-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine learning based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark datasets, but also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal mode prediction, high temperature molecular dynamics, dihedral torsional profile prediction and even bond breaking. We also demonstrate the smoothness, transferability and extrapolation capabilities of ACE on a new challenging benchmark dataset comprising a potential energy surface of a flexible drug-like molecule.

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Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias

Cited by 11 publications

References 27 publications

Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments

Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

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