Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions

Rakhimbekova, Assima; Madzhidov, Timur; Nugmanov, Ramil; Gimadiev, Timur; Baskin, Igor I.; Varnek, Alexandre

doi:10.3390/ijms21155542

Cited by 39 publications

(28 citation statements)

References 41 publications

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“…Given the importance of uncertainty quantification, a plethora of methods have been proposed so far and employed in various cheminformatics tasks such as molecular property prediction [ 7 ], chemical reaction prediction [ 8 ], material property prediction [ 9 ], NMR spectral property prediction [ 10 ] and interatomic potential prediction [ 11 ]. Broadly speaking, current mainstream uncertainty quantification methods used in the chemical domain can be divided into two categories: distance-based approaches and Bayesian approaches.…”

Section: Introductionmentioning

confidence: 99%

A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling

Wang

Chen

et al. 2021

J Cheminform

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Reliable uncertainty quantification for statistical models is crucial in various downstream applications, especially for drug design and discovery where mistakes may incur a large amount of cost. This topic has therefore absorbed much attention and a plethora of methods have been proposed over the past years. The approaches that have been reported so far can be mainly categorized into two classes: distance-based approaches and Bayesian approaches. Although these methods have been widely used in many scenarios and shown promising performance with their distinct superiorities, being overconfident on out-of-distribution examples still poses challenges for the deployment of these techniques in real-world applications. In this study we investigated a number of consensus strategies in order to combine both distance-based and Bayesian approaches together with post-hoc calibration for improved uncertainty quantification in QSAR (Quantitative Structure–Activity Relationship) regression modeling. We employed a set of criteria to quantitatively assess the ranking and calibration ability of these models. Experiments based on 24 bioactivity datasets were designed to make critical comparison between the model we proposed and other well-studied baseline models. Our findings indicate that the hybrid framework proposed by us can robustly enhance the model ability of ranking absolute errors. Together with post-hoc calibration on the validation set, we show that well-calibrated uncertainty quantification results can be obtained in domain shift settings. The complementarity between different methods is also conceptually analyzed.

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Section: Introductionmentioning

confidence: 99%

A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling

Wang

Chen

et al. 2021

J Cheminform

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“…A well validated predictive model requires a defined applicability domain (AD) for highlighting a part of the chemical space containing those compounds for which the model is supposed to provide reliable predictions [ 61 ]. Any predictive model needs to confirm the limitations with respect to its structural domain and response space.…”

Section: Methodsmentioning

confidence: 99%

Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer

et al. 2021

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Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is an important drug target for controlling cancer via epigenetic processes. In the present study, we have developed various predictive models for modeling the inhibitory activity of EZH2. Binary and multiclass models were built using SVM, random forest and XGBoost methods. Rigorous validation approaches including predictiveness curve, Y-randomization and applicability domain (AD) were employed for evaluation of the developed models. Eighteen descriptors selected from Boruta methods have been used for modeling. For binary classification, random forest and XGBoost achieved an accuracy of 0.80 and 0.82, respectively, on external test set. Contrastingly, for multiclass models, random forest and XGBoost achieved an accuracy of 0.73 and 0.75, respectively. 500 Y-randomization runs demonstrate that the models were robust and the correlations were not by chance. Evaluation metrics from predictiveness curve show that the selected eighteen descriptors predict active compounds with total gain (TG) of 0.79 and 0.59 for XGBoost and random forest, respectively. Validated models were further used for virtual screening and molecular docking in search of potential hits. A total of 221 compounds were commonly predicted as active with above the set probability threshold and also under the AD of training set. Molecular docking revealed that three compounds have reasonable binding energy and favorable interactions with critical residues in the active site of EZH2. In conclusion, we highlighted the potential of rigorously validated models for accurately predicting and ranking the activities of lead molecules against cancer epigenetic targets. The models presented in this study represent the platform for development of EZH2 inhibitors.

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“…The ISIDA fragment descriptors were generated for CGRs using the ISIDA Fragmentor 2017 software [ 32 ] wrapped by an in-house Python CIMtools library [ 33 ]. Atom-centered fragments based on sequences of atoms and bonds of fixed length ranged from two to four were used.…”

Section: Computational Proceduresmentioning

confidence: 99%

“…By default, the applicability domain (AD) of the models was not considered unless specifically mentioned. In that case, the Fragment Control approach [ 33 ] was applied. A given reaction was considered out of a model’s AD if its CGR contained a fragment absent in the training set CGRs.…”

Section: Computational Proceduresmentioning

confidence: 99%

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Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

Afonina

Mazitov

Nurmukhametova

et al. 2021

IJMS

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The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach, requiring one-to-one correspondence between chemical structure and a target property, can be used for optimal reaction conditions prediction only on a limited scale when only one condition component (e.g., catalyst or solvent) is considered. However, a particular reaction can proceed under several different conditions. In this paper, we describe the Likelihood Ranking Model representing an artificial neural network that outputs a list of different conditions ranked according to their suitability to a given chemical transformation. Benchmarking calculations demonstrated that our model outperformed some popular approaches to the theoretical assessment of reaction conditions, such as k Nearest Neighbors, and a recurrent artificial neural network performance prediction of condition components (reagents, solvents, catalysts, and temperature). The ability of the Likelihood Ranking model trained on a hydrogenation reactions dataset, (~42,000 reactions) from Reaxys® database, to propose conditions that led to the desired product was validated experimentally on a set of three reactions with rich selectivity issues.

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Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions

Cited by 39 publications

References 41 publications

A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling

A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling

Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer

Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach

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