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WIREs Comput Mol Sci
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Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

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Abstract Machine learning (ML) is becoming capable of transforming biomolecular interaction description and calculation, promising an impact on molecular and drug design, chemical biology, toxicology, among others. The first improvements can be seen from biomolecule structure prediction to chemical synthesis, molecular generation, mechanism of action elucidation, inverse design, polypharmacology, organ or issue targeting of compounds, property and multiobjective optimization. Chemical design proposals from an algorithm may be inventive and feasible. Challenges remain, with the availability, diversity, and quality of data being critical for developing useful ML models; marginal improvement seen in some cases, as well as in the interpretability, validation, and reuse of models. The ultimate aim of ML should be to facilitate options for the scientist to propose and undertake ideas and for these to proceed faster. Applications are ripe for transformative results in understudied, neglected, and rare diseases, where new data and therapies are strongly required. Progress and outlook on these themes are provided in this study. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Molecular Structures
A molecular design chemical Turing test, where a machine learning (ML), artificial intelligence (AI), or data science technique (a) proposes a chemical compound that to a chemist's (c) perception and judgment is indistinguishable from another chemist's (b) proposed structure
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Structure and Mechanism > Molecular Structures
Structure and Mechanism > Computational Biochemistry and Biophysics

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