Reconstruction of lossless molecular representations from fingerprints

The simplified molecular-input line-entry system (SMILES) is the most prevalent molecular representation used in AI-based chemical applications. However, there are innate limitations associated with the internal structure of SMILES representations. In this context, this study exploits the resolution and robustness of unique molecular representations, i.e., SMILES and SELFIES (SELF-referencIng Embedded strings), reconstructed from a set of structural fingerprints, which are proposed and used herein as vital representational tools for chemical and natural language processing (NLP) applications. This is achieved by restoring the connectivity information lost during fingerprint transformation with high accuracy. Notably, the results reveal that seemingly irreversible molecule-to-fingerprint conversion is feasible. More specifically, four structural fingerprints, extended connectivity, topological torsion, atom pairs, and atomic environments can be used as inputs and outputs of chemical NLP applications. Therefore, this comprehensive study addresses the major limitation of structural fingerprints that precludes their use in NLP models. Our findings will facilitate the development of text- or fingerprint-based chemoinformatic models for generative and translational tasks.This work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Nos. NRF-2019M3E5D4066898, NRF-2022R1C1C1005080 and NRF-2020M3A9G7103933 to I.A. and J.L.). This work was also supported by the Korea Environment Industry & Technology Institute (KEITI) through the Technology Development Project for Safety Management of Household Chemical Products, funded by the Korea Ministry of Environment (MOE) (KEITI:2020002960002 and NTIS:1485017120 to U.V.U. and J.L.)

Reconstruction of lossless molecular representations, SMILES and SELFIES, from fingerprints

SMILES is the most dominant molecular representation used in AI-based chemical applications, but it has innate limitations associated with its internal structure. Here, we exploit the idea that a set of structural fingerprints can be used as efficient alternatives to unique molecular representations. For this purpose, we trained neural-machine-translation based models that translate a set of various structural fingerprints to conventional text-based molecular representations, i.e., SMILES and SELFIES. The assessment of their conversion efficiency showed that our models successfully reconstructed molecules and achieved a high level of accuracy. Therefore, our approach brings structural fingerprints into play as strong representational tools in chemical natural language processing applications by restoring the connectivity information that is lost during fingerprint transformation. This comprehensive study addressed the major limitation of structural fingerprints, which precludes their implementation in NLP models. Our findings would facilitate the development of text or fingerprint-based chemoinformatic models for generative and translational tasks

Atom-in-SMILES tokenization

Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. In this study we show that the conventional SMILES tokenization itself is at fault, resulting in tokens that fail to reflect the true nature of molecules. To address this we propose atom-in-SMILES approach, resolving the ambiguities in the genericness of SMILES tokens. Our findings in multiple translation tasks suggest that proper tokenization has a great impact on the prediction quality. Considering the prediction accuracy and token degeneration comparisons, atom-in-SMILES appears as an effective method to draw higher quality SMILES sequences out of AI-based chemical models than other tokenization schemes. We investigate the token degeneration, highlight its pernicious influence on prediction quality, quantify the token-level repetitions, and include generated examples for qualitative analysis. We believe that atom-in-SMILES tokenization can readily be utilized by the community at large, providing chemically accurate, tailor-made tokens for molecular prediction models

Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization

Abstract Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a certain limitation that results in tokens failing to reflect the true nature of molecules. To address this issue, we developed the atom-in-SMILES tokenization scheme that eliminates ambiguities in the generic nature of SMILES tokens. Our results in multiple chemical translation and molecular property prediction tasks demonstrate that proper tokenization has a significant impact on prediction quality. In terms of prediction accuracy and token degeneration, atom-in-SMILES is more effective method in generating higher-quality SMILES sequences from AI-based chemical models compared to other tokenization and representation schemes. We investigated the degrees of token degeneration of various schemes and analyzed their adverse effects on prediction quality. Additionally, token-level repetitions were quantified, and generated examples were incorporated for qualitative examination. We believe that the atom-in-SMILES tokenization has a great potential to be adopted by broad related scientific communities, as it provides chemically accurate, tailor-made tokens for molecular property prediction, chemical translation, and molecular generative models

Retrosynthetic reaction pathway prediction through neural machine translation of atomic environments

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