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Cheminformatics

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Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics.

Chemical & pharmaceutical bulletin
Recently, remarkable progress has been achieved in artificial intelligence (AI), including machine learning. Various AI models have been proposed for drug discovery, including the design of small molecules, activity prediction, and three-dimensional ...

Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

Molecular diversity
In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach inv...

Outline and background for the EU-OS solubility prediction challenge.

SLAS discovery : advancing life sciences R & D
In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it...

Application of Transformers in Cheminformatics.

Journal of chemical information and modeling
By accelerating time-consuming processes with high efficiency, computing has become an essential part of many modern chemical pipelines. Machine learning is a class of computing methods that can discover patterns within chemical data and utilize this...

Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction.

Journal of chemical information and modeling
Chemical information disseminated in scientific documents offers an untapped potential for deep learning-assisted insights and breakthroughs. Automated extraction efforts have shifted from resource-intensive manual extraction toward applying machine ...

OpenChemIE: An Information Extraction Toolkit for Chemistry Literature.

Journal of chemical information and modeling
Information extraction from chemistry literature is vital for constructing up-to-date reaction databases for data-driven chemistry. Complete extraction requires combining information across text, tables, and figures, whereas prior work has mainly inv...

Stereoisomers Are Not Machine Learning's Best Friends.

Journal of chemical information and modeling
This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict the association constant between cyclodextrin and a guest. Identifying stereoisome...

Cheminformatics analysis of indoleamine and tryptophan 2,3-dioxygenase inhibitors: A descriptor and fingerprint based machine learning approach to disclose selectivity measures.

Computers in biology and medicine
Indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) are attractive drug targets for cancer immunotherapy. After disappointing results of the epacadostat as a selective IDO inhibitor in phase III clinical trials, there is much inter...

Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning.

Critical reviews in toxicology
This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and...