PTML Multi-Label Algorithms: Models, Software, and Applications

Bernabe       Ortega-Tenezaca; Viviana       Quevedo-Tumailli; Harbil       Bediaga; Jon       Collados; Sonia       Arrasate; Gotzon       Madariaga; Cristian    R    Munteanu; M.    Natália D.S.    Cordeiro; Humbert       González-Díaz

doi:10.2174/1568026620666200916122616

Abstract

By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to be predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.

Keywords: Drug Discovery, Cheminformatics, Multi-target models, Large data sets, PTML, Perturbation theory, Machine learning.

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DOI https://dx.doi.org/10.2174/1568026620666200916122616	Print ISSN 1568-0266
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