Molecules are complex dynamic objects that can exist in different molecular forms
(conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known …

This chapter is a review of the most recent developments in the field of pharmacophore
modeling, covering both methodology and application. Pharmacophore-based virtual …

This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …

Strategies for drug discovery and repositioning are urgently need with respect to COVID-19.
Here we present REDIAL-2020, a suite of computational models for estimating small …

In this paper we illustrate the application of the ISIDA (In SIlico design and Data Analysis)
software to perform virtual screening of large databases of compounds and reactions and to …

Background In silico target prediction of compounds plays an important role in drug
discovery. The chemical similarity ensemble approach (SEA) is a promising method, which …

The present work proposes a unified conceptual framework to describe and quantify the
important issue of the Applicability Domains (AD) of Quantitative Structure− Activity …

Abstract ISIDA Property‐Labelled Fragment Descriptors (IPLF) were introduced as a general
framework to numerically encode molecular structures in chemoinformatics, as counts of …

MayaChemTools is a growing collection of Perl scripts, modules, and classes to support a
variety of computational drug discovery needs, such as manipulation and analysis of data …

Structural fingerprints and pharmacophore modeling are methodologies that have been
used for at least 2 decades in various fields of cheminformatics, from similarity searching to …