Chemical biology--identification of small molecule modulators of cellular activity by natural product inspired synthesis

The aim of this tutorial review is to introduce the reader to the concept, synthesis and application of natural product-inspired compound collections as an important field in chemical biology. This review will discuss how potentially interesting scaffolds can be identified (structural classification of natural products), synthesized in an appropriate manner (including stereoselective transformations for solid phase-bound compounds) and tested in biological assays (cell-based screening as well as biochemical in vitro assays). These approaches will provide the opportunity to identify new and interesting compounds as well as new targets for chemical biology and medicinal chemistry research.     

Free resources to assist structure-based virtual ligand screening experiments

In today's research environment, a wealth of experimental/theoretical structural data is available and the number of therapeutically relevant macromolecular structures is growing rapidly. This, coupled with the huge number of small non-peptide potential drug candidates easily available (over 7 million compounds), highlight the need of using computer-aided techniques for the efficient identification and optimization of novel hit compounds. Virtual (or in silico) ligand screening based on the three-dimensional structure of macromolecular targets (SB-VLS) is firmly established as an important approach to identify chemical entities that have a high likelihood of binding to a target molecule to elicit desired biological responses. A myriad of free applications and services facilitating the drug discovery process have been posted on the Web. In this review, we cite over 350 URLs that are useful for SB-VLS projects and essentially free for academic groups. We attempt to provide links for in silico ADME/tox prediction tools, compound collections, some ligand-based methods, characterization/simulation of 3D targets and homology modeling tools, druggable pocket predictions, active site comparisons, analysis of macromolecular interfaces, protein docking tools to help identify binding pockets and protein-ligand docking/scoring methods. As such, we aim at providing both, methods pertaining to the field of Structural Bioinformatics (defined here as tools to study macromolecules) and methods pertaining to the field of Chemoinformatics (defined here as tools to make better decisions faster in the arena of drug/lead identification and optimization). We also report several recent success stories using these free computer methods. This review should help readers finding free computer tools useful for their projects. Overall, we are confident that these tools will facilitate rapid and cost-effective identification of new hit compounds. The URLs presented in this review will be updated regularly at www.vls3d.com in the coming months, "Links" section.     

"In-house likeness": comparison of large compound collections using artificial neural networks

Binary classification models able to discriminate between data sets of compounds are useful tools in a range of applications from compound acquisition to library design. In this paper we investigate the ability of artificial neural networks to discriminate between compound collections from various sources aiming at developing an "in-house likeness" scoring scheme (i.e. in-house vs external compounds) for compound acquisition. Our analysis shows atom-type based Ghose-Crippen fingerprints in combination with artificial neural networks to be an efficient way to construct such filters. A simple measure of the chemical overlap between different compound collections can be derived using the output scores from the neural net models.     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

Selective N-terminal modification of peptides and proteins: Recent progresses and applications

Numerous strategies for linking desired chemical probes with target peptides and proteins have been developed and applied in the field of biological chemistry. Approaches for site-specific modification of native amino acid residues in test tubes and biological contexts represent novel biological tools for understanding the role of peptides and proteins. Selective N-terminal modification strategies have been broadly studied especially in the last 10 years, as N-terminal positions are typically so...     

Target identification strategies in chemical genetics

Chemical inhibitors have had a profound impact on many diverse fields of biology. The goal of chemical genetics is to use small molecules to perturb biological systems in a manner conceptually similar to traditional genetics. Key to the advancement of the chemical genetic paradigm is the further development of tools and approaches for the identification of the protein targets of active compounds identified in chemical genetic screens. This review will address historic examples in which forward chemical genetics yielded new insight into a biological problem through successful identification of the target of an active molecule. The approaches covered have been grouped into two broad classes: target identification by affinity-based methods and target identification by deduction. Strengths and shortcomings of each approach as it pertains to their application to modern chemical genetics will be discussed. Finally, a series of new genomic and proteomic-based techniques for target identification will be described. Although a truly general approach to target identification has yet to be developed, these examples illustrate that there are many effective strategies for successfully elucidating the biological targets of active small molecules.     

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.     

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.     

Molecular Design in Practice: A Review of Selected Projects in a French Research Institute That Illustrates the Link between Chemical Biology and Medicinal Chemistry

Chemical biology and drug discovery are two scientific activities that pursue different goals but complement each other. The former is an interventional science that aims at understanding living systems through the modulation of its molecular components with compounds designed for this purpose. The latter is the art of designing drug candidates, i.e., molecules that act on selected molecular components of human beings and display, as a candidate treatment, the best reachable risk benefit ratio. In chemical biology, the compound is the means to understand biology, whereas in drug discovery, the compound is the goal. The toolbox they share includes biological and chemical analytic technologies, cell and whole-body imaging, and exploring the chemical space through state-of-the-art design and synthesis tools. In this article, we examine several tools shared by drug discovery and chemical biology through selected examples taken from research projects conducted in our institute in the last decade. These examples illustrate the design of chemical probes and tools to identify and validate new targets, to quantify target engagement in vitro and in vivo, to discover hits and to optimize pharmacokinetic properties with the control of compound concentration both spatially and temporally in the various biophases of a biological system.