Machine Learning guided early drug discovery of small molecules

Pillai, Nikhil; Dasgupta, Aparajita; Sudaskorn, Sirimas; Fretland, Jennifer; Mavroudis, Panteleimon D.

doi:10.1016/j.drudis.2022.03.017

Cited by 38 publications

(22 citation statements)

References 42 publications

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“…Recently, computer-aided drug design using in silico models to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters has been widely implemented in the field of drug discovery and development. , This approach is effective for evaluating the physicochemical properties and in vivo pharmacokinetics during the early stages of drug discovery. , In addition, the use of in silico prediction techniques is expected to minimize the expenses and risks of subsequent withdrawals during clinical trials. To the best of our knowledge, there have been no previous reports on predicting in vivo fm from structural information alone.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

et al. 2022

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The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug–drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.

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Section: Introductionmentioning

confidence: 99%

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

et al. 2022

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“…A brief section is devoted to describe the recent progress witnessed in drug repurposing methods for COVID-19. For a more comprehensive review on drug discovery, readers can refer reviews by Dara et al ( 2022 ), Kolluri et al ( 2022 ), Shehab et al ( 2022 ) and Pillai et al ( 2022 ). Though, drug discovery is described separately, the understanding of organic synthesis is symbiotic with this field.…”

Section: Facilitating Drug Discovery and Repurposingmentioning

confidence: 99%

Navigating with chemometrics and machine learning in chemistry

Joshi¹

2023

Artif Intell Rev

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Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.

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“…Machine learning models have also been widely used in the last few years to predict properties of molecules based on their structure. In particular, in drug discovery such methods have been shown to be very successful [ 30 ]. Numerical values describing the molecular structure have also been used to predict for example sweetness [ 31 ] or bitterness [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Prediction of the Formation of Co-Amorphous Systems

Fink

Brunsteiner²,

Mitsche

et al. 2023

Pharmaceutics

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Co-amorphous systems (COAMS) have raised increasing interest in the pharmaceutical industry, since they combine the increased solubility and/or faster dissolution of amorphous forms with the stability of crystalline forms. However, the choice of the co-former is critical for the formation of a COAMS. While some models exist to predict the potential formation of COAMS, they often focus on a limited group of compounds. Here, four classes of combinations of an active pharmaceutical ingredient (API) with (1) another API, (2) an amino acid, (3) an organic acid, or (4) another substance were considered. A model using gradient boosting methods was developed to predict the successful formation of COAMS for all four classes. The model was tested on data not seen during training and predicted 15 out of 19 examples correctly. In addition, the model was used to screen for new COAMS in binary systems of two APIs for inhalation therapy, as diseases such as tuberculosis, asthma, and COPD usually require complex multidrug-therapy. Three of these new API-API combinations were selected for experimental testing and co-processed via milling. The experiments confirmed the predictions of the model in all three cases. This data-driven model will facilitate and expedite the screening phase for new binary COAMS.

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Machine Learning guided early drug discovery of small molecules

Cited by 38 publications

References 42 publications

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

Navigating with chemometrics and machine learning in chemistry

Data-Driven Prediction of the Formation of Co-Amorphous Systems

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