Literature DB >> 30472428

Analysis of solvent-exposed and buried co-crystallized ligands: a case study to support the design of novel protein-protein interaction inhibitors.

Daniela Trisciuzzi1, Orazio Nicolotti1, Maria A Miteva2, Bruno O Villoutreix3.   

Abstract

Molecular descriptors have been used to characterize and predict the functions of small molecules, including inhibitors of protein-protein interactions (iPPIs). Such molecules are valuable to investigate disease pathways and as starting points for drug discovery endeavors. iPPIs tend to bind at the surface of macromolecules and the design of such compounds remains challenging. Here, we report on our investigation of a pool of interpretable molecular descriptors for solvent-exposed and buried co-crystallized ligands. Several descriptors were found to be significantly different between the two classes and were further exploited using machine-learning approaches. This work could open new perspectives for the rational design of focused libraries enriched in new types of small drug-like molecules that could be used to prevent PPIs.
Copyright © 2018 Elsevier Ltd. All rights reserved.

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Year:  2018        PMID: 30472428     DOI: 10.1016/j.drudis.2018.11.013

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  2 in total

1.  PLATO: A Predictive Drug Discovery Web Platform for Efficient Target Fishing and Bioactivity Profiling of Small Molecules.

Authors:  Fulvio Ciriaco; Nicola Gambacorta; Daniela Trisciuzzi; Orazio Nicolotti
Journal:  Int J Mol Sci       Date:  2022-05-08       Impact factor: 6.208

2.  New machine learning and physics-based scoring functions for drug discovery.

Authors:  Isabella A Guedes; André M S Barreto; Diogo Marinho; Eduardo Krempser; Mélaine A Kuenemann; Olivier Sperandio; Laurent E Dardenne; Maria A Miteva
Journal:  Sci Rep       Date:  2021-02-04       Impact factor: 4.379

  2 in total

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