Methods for combinatorial and parallel library design

Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1

Sphingosine kinases (SphKs) are a group of important enzymes that circulate at low micromolar concentrations in mammals and have received considerable attention due to the roles they play in a broad array of biological processes including apoptosis, mutagenesis, lymphocyte migration, radio- and chemo-sensitization, and angiogenesis. In the present study, we constructed three classification models by four machine learning (ML) algorithms including naive bayes (NB), support vector machine (SVM), logistic regression, and random forest from 395 compounds. The generated ML models were validated by fivefold cross validation. Five different scaffold hit fragments resulted from SVM model-based virtual screening and docking results indicate that all the five fragments exhibit common hydrogen bond interaction a catalytic residue of SphK1. Further, molecular dynamics (MD) simulations and binding free energy calculation had been carried out with the identified five fragment leads and three cocrystal inhibitors. The best 15 fragments were selected. Molecular dynamics (MD) simulations showed that among these compounds, 7 compounds have favorable binding energy compared with cocrystal inhibitors. Hence, the study showed that the present lead fragments could act as potential inhibitors against therapeutic target of cancers and neurodegenerative disorders.

Screening Library Design

Thanks to technological advances and a greater understanding of the biological and chemical natures of targets and related diseases, high-throughput screening (HTS) has been allowed to be faster, cheaper, and more accessible. Yet, despite these increased technologies and understandings, the frequency of novel and drugs are being approved each year has not being increasing over the years. 2017 was considered a "bumper" year with a total of 46 approved drugs, over double that of the previous year. However, it is thought that part of the problem that HTS has not lived up to expectations is because of the contents of current chemical libraries. Therefore, new methods to design screening libraries are of great interest.

The concept of privileged structures in rational drug design: focus on acridine and quinoline scaffolds in neurodegenerative and protozoan diseases

INTRODUCTION

For nearly 20 years, privileged structures have offered an optimal source of core scaffolds and capping fragments for the design of combinatorial libraries directed at a broad spectrum of targets. From describing structures promiscuous within a given target family, the concept has evolved to include frameworks that can modulate proteins lacking a strict target class relation.

AREAS COVERED

Based on a literature search from 2000 to 2010, we discuss how two privileged motifs, quinolines and acridines, are particularly recurrent in compounds active against two quite different pathologies, neurodegenerative and protozoan diseases.

EXPERT OPINION

As privileged structures, quinolines and acridines could improve the productivity of drug discovery projects in the field of neurodegenerative and protozoan diseases. They could be particularly relevant for protozoan diseases because of the importance of cost-effective strategies and less stringent intellectual property concerns. Furthermore, because of their inherent affinity for various targets, privileged structures could offer a viable starting point in the search for novel multi-target ligands. Finally, from a broader perspective, they can serve as effective probes for investigating unknown but interrelated mechanisms of action.