An Ab Initio Method for Designing Multi‐Target Specific Pharmacophores using Complementary Interaction Field of Aspartic Proteases

Abstract

For past few decades, key objectives of rational drug discovery have been the designing of specific and selective ligands for target proteins. Infectious diseases like malaria are continuously becoming resistant to traditional medicines, which inculcates need for new approaches to design inhibitors for antimalarial targets. A novel method for ab initio designing of multi target specific pharmacophores using the interaction field maps of active sites of multiple proteins has been developed to design 'specificity' pharmacophores for aspartic proteases. The molecular interaction field grid maps of active sites of aspartic proteases (plasmepsin II & IV from Plasmodium falciparum, plasmepsin from Plasmodium vivax, pepsin & cathepsin D from human) are calculated and common pharmacophoric features for favourable binding spots in active sites are extracted in the form of cliques of graphs using inductive logic programming (ILP). The two pharmacophore ensembles are constructed from largest common cliques by imposing size of receptor active site (L) and domain-specific receptor-ligand information (S). The overlap of chemical space between two ensembles and the results of virtual screening of inhibitor database with known activities show that this method can design efficient pharmacophores with no prior ligand information.

ILP-assisted de novo drug design

“…A 1-dimensional example is shown in Fig. 2a-d [from Kaalia et al (2015): in reality, the MIF is a three-dimensional surface]. In Fig.…”

“…Based on the long experience of one of the authors in the pharmaceutical industry 3 , and a recent publication in the chemical literature (Kaalia et al 2015) we have good reasons to believe that these are significant contributions to the area of drug-design. To the field of ILP, this paper's contributions are as follows:…”

“…The work presented here is a substantial extension of the work in Kaalia et al (2015). The principal differences are these: (a) Here, the focus is on the use of ILP for the problem.…”

“…The principal differences are these: (a) Here, the focus is on the use of ILP for the problem. To this end, we have included algorithmic descriptions of the procedures used to find frequent cliques; (b) In Kaalia et al (2015), only maximally-specific pharmacophores are considered. Here we provide a method of extending this to more general pharmacophores using the notion of quasi-cliques; (c) The assessment in Kaalia et al (2015) are largely of a qualitative nature, focusing on the chemistry implied by the results.…”

“…To this end, we have included algorithmic descriptions of the procedures used to find frequent cliques; (b) In Kaalia et al (2015), only maximally-specific pharmacophores are considered. Here we provide a method of extending this to more general pharmacophores using the notion of quasi-cliques; (c) The assessment in Kaalia et al (2015) are largely of a qualitative nature, focusing on the chemistry implied by the results. This paper contains quantitative assessments of results in a manner familiar to researchers in machine learning; (d) We have included comparisons against a Baseline variant that allows us to assess the role of domainknowledge; and (e) We have created an additional set of decoys and present results on this set, that allows us to compare against a random choice approach.…”