SYBA: Bayesian estimation of synthetic accessibility of organic compounds

Voršilák, Milan; Kolář, Michal; Čmelo, Ivan; Svozil, Daniel

doi:10.21203/rs.2.22597/v1

Cited by 9 publications

(23 citation statements)

References 33 publications

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“…This procedure was previously optimized [45] to ensure that though generated molecules can be deemed as HS, they are not excessively complex. Nonpher algorithm and compound set construction are described in a detail in the Nonpher and SYBA publications [37,45].…”

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

“…7) against the reference merged_dbs compound set using ECFP4 fingerprint 1 024 bits long. ZRFT profiles are compared with the distribution of two fragment based synthetic accessibility measures: SAScore [48] and SYBA [37]. SAScore is calculated by the RDKit toolkit [33] and SYBA by the syba Python package [49].…”

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

“…In addition, following our previous work on synthetic accessibility assessment [37,45], ZRFT is also applied for the classification of compounds as either ES or HS. ZRFT classification results are compared with random forest (RF) classifier, SAScore and SYBA using the T MC and T CP test sets [37] (Additional file 3). The T MC test set was manually curated from the literature and it consists of 40 HS compounds assessed by experienced medicinal chemists [48,[50][51][52] and of 40 ES compounds randomly selected from the ZINC15 database [25].…”

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

“…The T MC test set was manually curated from the literature and it consists of 40 HS compounds assessed by experienced medicinal chemists [48,[50][51][52] and of 40 ES compounds randomly selected from the ZINC15 database [25]. Because small T MC size may bias results, 30 different T MC data set instances were generated using the same HS compounds, but different ES compounds [37]. The computationally picked T CP test set consists of 3 581 excessively complex (i.e., HS) compounds from the GDB-17 database [53] supplemented by 3 581 ES compounds randomly selected from the ZINC15 database [25].…”

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

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Profiling and analysis of chemical compounds using pointwise mutual information

2021

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Pointwise mutual information (PMI) is a measure of association used in information theory. In this paper, PMI is used to characterize several publicly available databases (DrugBank, ChEMBL, PubChem and ZINC) in terms of association strength between compound structural features resulting in database PMI interrelation profiles. As structural features, substructure fragments obtained by coding individual compounds as MACCS, PubChemKey and ECFP fingerprints are used. The analysis of publicly available databases reveals, in accord with other studies, unusual properties of DrugBank compounds which further confirms the validity of PMI profiling approach. Z-standardized relative feature tightness (ZRFT), a PMI-derived measure that quantifies how well the given compound’s feature combinations fit these in a particular compound set, is applied for the analysis of compound synthetic accessibility (SA), as well as for the classification of compounds as easy (ES) and hard (HS) to synthesize. ZRFT value distributions are compared with these of SYBA and SAScore. The analysis of ZRFT values of structurally complex compounds in the SAVI database reveals oligopeptide structures that are mispredicted by SAScore as HS, while correctly predicted by ZRFT and SYBA as ES. Compared to SAScore, SYBA and random forest, ZRFT predictions are less accurate, though by a narrow margin (AccZRFT = 94.5%, AccSYBA = 98.8%, AccSAScore = 99.0%, AccRF = 97.3%). However, ZRFT ability to distinguish between ES and HS compounds is surprisingly high considering that while SYBA, SAScore and random forest are dedicated SA models, ZRFT is a generic measurement that merely quantifies the strength of interrelations between structural feature pairs. The results presented in the current work indicate that structural feature co-occurrence, quantified by PMI or ZRFT, contains a significant amount of information relevant to physico-chemical properties of organic compounds.

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Section: Synthetic Accessibility Analysismentioning

confidence: 99%

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

Section: Synthetic Accessibility Analysismentioning

confidence: 99%

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Profiling and analysis of chemical compounds using pointwise mutual information

2021

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Machine Learning Descriptors for Data‐Driven Catalysis Study

Mou

Han

Smith³

et al. 2023

Advanced Science

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Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

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Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

Bennett

Szczypiński

Turcani

et al. 2021

J. Chem. Inf. Model.

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Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as “easy-to-synthesize” or “difficult-to-synthesize” by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties.

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SYBA: Bayesian estimation of synthetic accessibility of organic compounds

Cited by 9 publications

References 33 publications

Profiling and analysis of chemical compounds using pointwise mutual information

Profiling and analysis of chemical compounds using pointwise mutual information

Machine Learning Descriptors for Data‐Driven Catalysis Study

Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

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