Machine learning in chemoinformatics and drug discovery.

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.

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Computational drug discovery

Scaffold diversity of natural products: inspiration for combinatorial library design

ShaEP is a tool for rigid-body superimposition and similarity evaluation of ligand-sized molecules. Molecular overlay methods traditionally work on either substructures, molecular surfaces or interaction fields, or atom-centered Gaussian functions representing the molecular volume. While substructure searches are unlikely to reveal hits that are chemically different from the template structure, the other methods are capable of “scaffold hopping”. Methods that match characteristic points in interaction fields can find alignments in situations where only some portions of the structures match but potentially miss good alignments if the used point sets are not detailed enough, which in turn increases the runtime of the used graph algorithms beyond practical limits. The faster, polynomially scaling volumetric methods consider the whole space to be equally important, which works well for molecules of equal size but partial matches might go undetected. ShaEP aims to capture the strengths of both field-based and ...

ShaEP: molecular overlay based on shape and electrostatic potential.

One approach to experimental science involves creating hypotheses, then testing them by varying one or more independent variables and assessing the effects of this variation on the processes of interest. We use this strategy to compare the intellectual status and available evidence for two models or views of mechanisms of transmembrane drug transport into intact biological cells. One (BDII) asserts that lipoidal phospholipid Bilayer Diffusion Is Important, while a second (PBIN) proposes that in normal intact cells Phospholipid Bilayer diffusion Is Negligible (i.e. may be neglected quantitatively), because evolution selected against it, and with transmembrane drug transport being effected by genetically encoded proteinaceous carriers or pores, whose ‘natural’ biological roles and substrates are based in intermediary metabolism. Despite a recent review elsewhere, we can find no evidence able to support BDII as we can find no experiments in intact cells in which phospholipid bilayer diffusion was either varied independently or measured directly (although there are many papers where it was inferred by seeing a covariation of other dependent variables). By contrast, we find an abundance of evidence showing cases in which changes in the activities of named and genetically identified transporters led to measurable changes in the rate or extent of drug uptake. PBIN also has considerable predictive power, and accounts readily for the large differences in drug uptake between tissues, cells and species, in accounting for the metabolite-likeness of marketed drugs, in pharmacogenomics, and in providing a straightforward explanation for the late-stage appearance of toxicity and of lack of efficacy during drug discovery programmes despite macroscopically adequate pharmacokinetics. Consequently, the view that Phospholipid Bilayer diffusion Is Negligible (PBIN) provides a starting hypothesis for assessing cellular drug uptake that is much better supported by the available evidence,

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How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion.

This review is dedicated to a survey on molecular similarity and diversity. Key findings reported in recent investigations are selectively highlighted and summarized. Even if this overview is mainly centered in chemoinformatics, applications in other areas (pharmaceutical and medical chemistry, combinatorial chemistry, chemical databases management, etc.) are also introduced. The approaches used to define and descript the concepts of molecular similarity and diversity in the context of chemoinformatics are discussed in the first part of this review. We introduce, in the second and third parts, the descriptions and analyses of different methods and techniques. Finally, current applications and problems are enumerated and discussed in the last part.

Botao Fan

Papers

Molecular similarity and diversity in chemoinformatics: from theory to applications.

SIRS-SS: A System for Simulating IR/Raman Spectra. Part 2. Procedures and Performance.

SIRS-SS: A System for Simulating IR/Raman Spectra. Part 1. Substructure/Subspectrum Correlation.