Computational design strategies for combinatorial libraries

Druggable targets from coronaviruses for designing new antiviral drugs

Severe respiratory infections were highlighted in the SARS-CoV outbreak in 2002, as well as MERS-CoV, in 2012. Recently, the novel CoV (COVID-19) has led to severe respiratory damage to humans and deaths in Asia, Europe, and Americas, which allowed the WHO to declare the pandemic state. Notwithstanding all impacts caused by Coronaviruses, it is evident that the development of new antiviral agents is an unmet need. In this review, we provide a complete compilation of all potential antiviral agents targeting macromolecular structures from these Coronaviruses (Coronaviridae), providing a medicinal chemistry viewpoint that could be useful for designing new therapeutic agents.

Comprehensive Prediction of Molecular Recognition in a Combinatorial Chemical Space Using Machine Learning

In combinatorial chemical approaches, optimizing the composition and arrangement of building blocks toward a particular function has been done using a number of methods, including high throughput molecular screening, molecular evolution, and computational prescreening. Here, a different approach is considered that uses sparse measurements of library molecules as the input to a machine learning algorithm which generates a comprehensive, quantitative relationship between covalent molecular structure and function that can then be used to predict the function of any molecule in the possible combinatorial space. To test the feasibility of the approach, a defined combinatorial chemical space consisting of ∼10¹² possible linear combinations of 16 different amino acids was used. The binding of a very sparse, but nearly random, sampling of this amino acid sequence space to 9 different protein targets is measured and used to generate a general relationship between peptide sequence and binding for each target. Surprisingly, measuring as little as a few hundred to a few thousand of the ∼10¹² possible molecules provides sufficient training to be highly predictive of the binding of the remaining molecules in the combinatorial space. Furthermore, measuring only amino acid sequences that bind weakly to a target allows the accurate prediction of which sequences will bind 10-100 times more strongly. Thus, the molecular recognition information contained in a tiny fraction of molecules in this combinatorial space is sufficient to characterize any set of molecules randomly selected from the entire space, a fact that potentially has significant implications for the design of new chemical function using combinatorial chemical libraries.

Combinatorial chemistry in drug discovery

Several combinatorial methods have been developed to create focused or diverse chemical libraries with a wide range of linear or macrocyclic chemical molecules: peptides, non-peptide oligomers, peptidomimetics, small-molecules, and natural product-like organic molecules. Each combinatorial approach has its own unique high-throughput screening and encoding strategy. In this article, we provide a brief overview of combinatorial chemistry in drug discovery with emphasis on recently developed new technologies for design, synthesis, screening and decoding of combinatorial library. Examples of successful application of combinatorial chemistry in hit discovery and lead optimization are given. The limitations and strengths of combinatorial chemistry are also briefly discussed. We are now in a better position to truly leverage the power of combinatorial technologies for the discovery and development of next-generation drugs.

Methods for combinatorial and parallel library design

Diversity has historically played a critical role in design of combinatorial libraries, screening sets and corporate collections for lead discovery. Large library design dominated the field in the 1990s with methods ranging anywhere from purely arbitrary through property based reagent selection to product based approaches. In recent years, however, there has been a downward trend in library size. This was due to increased information about the desirable targets gleaned from the genomics revolution and to the ever growing availability of target protein structures from crystallography and homology modeling. Creation of libraries directed toward families of receptors such as GPCRs, kinases, nuclear hormone receptors, proteases, etc., replaced the generation of libraries based primarily on diversity while single target focused library design has remained an important objective. Concurrently, computing grids and cpu clusters have facilitated the development of structure based tools that screen hundreds of thousands of molecules. Smaller "smarter" combinatorial and focused parallel libraries replaced those early un-focused large libraries in the twenty-first century drug design paradigm. While diversity still plays a role in lead discovery, the focus of current library design methods has shifted to receptor based methods, scaffold hopping/bio-isostere searching, and a much needed emphasis on synthetic feasibility. Methods such as "privileged substructures based design" and pharmacophore based design still are important methods for parallel and small combinatorial library design. This chapter discusses some of the possible design methods and presents examples where they are available.

Emerging Chemical Patterns: A New Methodology for Molecular Classification and Compound Selection

A concept termed Emerging Chemical Patterns (ECPs) is introduced as a novel approach to molecular classification. The methodology makes it possible to extract key molecular features from very few known active compounds and classify molecules according to different potency levels. The approach was developed in light of the situation often faced during the early stages of lead optimization efforts: too few active reference molecules are available to build computational models for the prediction of potent compounds. The ECP method generates high-resolution signatures of active compounds. Predictive ECP models can be built based on the information provided by sets of only three molecules with potency in the nanomolar and micromolar range. In addition to individual compound predictions, an iterative ECP scheme has been designed. When applied to different sets of active molecules, iterative ECP classification produced compound selection sets with increases in average potency of up to 3 orders of magnitude.

Topological side-chain classification of β-turns: Ideal motifs for peptidomimetic development

Beta-turns are important topological motifs for biological recognition of proteins and peptides. Organic molecules that sample the side chain positions of beta-turns have shown broad binding capacity to multiple different receptors, for example benzodiazepines. Beta-turns have traditionally been classified into various types based on the backbone dihedral angles (phi2, psi2, phi3 and psi3). Indeed, 57-68% of beta-turns are currently classified into 8 different backbone families (Type I, Type II, Type I', Type II', Type VIII, Type VIa1, Type VIa2 and Type VIb and Type IV which represents unclassified beta-turns). Although this classification of beta-turns has been useful, the resulting beta-turn types are not ideal for the design of beta-turn mimetics as they do not reflect topological features of the recognition elements, the side chains. To overcome this, we have extracted beta-turns from a data set of non-homologous and high-resolution protein crystal structures. The side chain positions, as defined by C(alpha)-C(beta) vectors, of these turns have been clustered using the kth nearest neighbor clustering and filtered nearest centroid sorting algorithms. Nine clusters were obtained that cluster 90% of the data, and the average intra-cluster RMSD of the four C(alpha)-C(beta) vectors is 0.36. The nine clusters therefore represent the topology of the side chain scaffold architecture of the vast majority of beta-turns. The mean structures of the nine clusters are useful for the development of beta-turn mimetics and as biological descriptors for focusing combinatorial chemistry towards biologically relevant topological space.

Predicting permeability coefficient in ADMET evaluation by using different membranes-interaction QSAR

Membrane-interaction quantitative structure activity relationship (MI-QSAR) analysis was applied to a data set with 18 compounds in 18 different membranes. MI-QSAR was used to estimate the ADMET properties including the transport of organic solutes through biological membranes. The most important descriptors are the aqueous solvation free energy, FH2O, and diffusion coefficient for all membranes. The correlation coefficient, r2, and cross-validation correlation coefficient, q2, for DMPG membrane is 0.850 and 0.770, respectively. The relationship between FH2O and permeability is nonlinear. But the detail effect of aqueous solvation free energy and diffusion coefficient to the permeability depends on the type of membrane. The final models also support the solution-diffusion mechanism of transport is important in membrane.

Novel Statistical Approach for Primary High-Throughput Screening Hit Selection

The standard activity threshold-based method (the "top X" approach), currently widely used in the high-throughput screening (HTS) data analysis, is ineffective at identifying good-quality hits. We have proposed a novel knowledge-based statistical approach, driven by the hidden structure-activity relationship (SAR) within a screening library, for primary hit selection. Application to an in-house ultrahigh-throughput screening (uHTS) campaign has demonstrated it can directly identify active scaffolds containing valuable SAR information with a greatly improved confirmation rate compared to the standard "top X" method (from 55% to 85%). This approach may help produce high-quality leads and expedite the hit-to-lead process in drug discovery.

There is no such thing as ‘diversity’!

Within the context of the increasing application of combinatorial methodology, the term 'diversity' has gained significant importance. The general understanding of this term is that diversity describes the degree of dissimilarity within a set of chemical structures. This Opinion article proposes that this understanding is superficial at best and irrelevant at worst. It is argued that relevant diversity can only be measured by the application of external criteria (such as a biological assay), which can discriminate the different structures by their different behaviour within this external context. According to this understanding, the diversity of a collection is highly dependent on the applied criteria. Therefore, a relevant diversity of chemical structures, per se, does not exist.

Exploring the chemogenomic knowledge space with annotated chemical libraries

The recent human genome initiatives have led to the discovery of a multitude of genes that are potentially associated with various pathologic conditions and, thus, have opened new horizons in drug discovery. Simultaneously, annotated chemical libraries have emerged as information-rich databases to integrate biological and chemical data. They can be useful for the discovery of new pharmaceutical leads, the validation of new biotargets and the determination of the structural basis of ligand selectivity within target families. Annotated libraries provide a strong information basis for computational design of target-directed combinatorial libraries, which are a key component of modern drug discovery. Today, the rational design of chemical libraries enhanced with chemogenomics data is a new area of progressive research.

Applying combinatorial chemistry and biology to food research

In the past decade combinatorial chemistry has become a major focus of research activity in the pharmaceutical industry for accelerating the development of novel therapeutic compounds. The same combinatorial strategies could be applied to a broad spectrum of areas in agricultural and food research, including food safety and nutrition, development of product ingredients, and processing and conversion of natural products. In contrast to "rational design", the combinatorial approach relies on molecular diversity and high-throughput screening. The capability of exploring the structural and functional limits of a vast population of diverse chemical and biochemical molecules makes it possible to expedite the creation and isolation of compounds of desirable and useful properties. Several studies in recent years have demonstrated the utility of combinatorial methods for food research. These include the discovery of synthetic antimicrobial, antioxidative, and aflatoxin-binding peptides, the identification and analysis of unique flavor compounds, the generation of new enzyme inhibitors, the development of therapeutic antibodies for botulinum neurotoxins, the synthesis of unnatural polyketides and carotenoids, and the modification of food enzymes with novel properties. The results of such activities could open a large area of applications with potential benefits to the food industry. This review describes the current techniques of combinatorial chemistry and their applications, with emphasis on examples in food science research.

Lead- and drug-like compounds: the rule-of-five revolution

Citations in CAS SciFinder to the rule-of-five (RO5) publication will exceed 1000 by year-end 2004. Trends in the RO5 literature explosion that can be discerned are the further definitions of drug-like. This topic is explored in terms of drug-like physicochemical features, drug-like structural features, a comparison of drug-like and non-drug-like in drug discovery and a discussion of how drug-like features relate to clinical success. Physicochemical features of CNS drugs and features related to CNS blood-brain transporter affinity are briefly reviewed. Recent literature on features of non-oral drugs is reviewed and how features of lead-like compounds differ from those of drug-like compounds is discussed. Most recently, partly driven by NIH roadmap initiatives, considerations have arisen as to what tool-like means in the search for chemical tools to probe biology space. All these topics frame the scope of this short review/perspective.:

Integrating cheminformatic analysis in combinatorial chemistry

Cheminformatic analysis of drug-related compound databases has enabled the identification of the physicochemical properties that have the greatest influence on determining the drug-like characteristics of a compound. This enables definition of the parameters and profiles used in constructing a high-quality combinatorial library. Awareness of the multi-objective nature of combinatorial library construction has also given rise to techniques designed to enhance the likelihood of including the best compounds in a given library.

Ligand identification for G-protein-coupled receptors: a lead generation perspective

This review addresses strategies for the generation of ligands for G-protein-coupled receptors outside classical high-throughput screening and literature based approaches. These range from the chemical intuition-based strategies of endogenous ligand elaboration and privileged structure decoration to the in silico approaches of virtual screening and de novo design. Examples are cited where supporting pharmacological data has been presented.

Applying data mining techniques to library design, lead generation and lead optimization

Many data mining techniques have been applied to activity and ADMET datasets and the resulting models are being used to understand quantitative structure-activity relationships and design new libraries. This review summarizes data mining concepts and discuss their application to library design, lead generation (particularly for sequential screening) and lead optimization (specifically for generating and interpreting QSAR models). Also, this review discusses recent comparative studies between data mining techniques and draws some conclusions about the patterns emerging in the drug discovery data mining field.

Libraries from natural product-like scaffolds

Natural products are an attractive source of varied structures that exhibit potent biological activities, and desirable pharmacological profiles. Since the relatively recent advent of high-throughput organic synthesis in the drug discovery process, several design approaches have been applied to the construction of screening libraries. Libraries of natural-product derivatives, natural-product-like compounds prepared by total synthesis, and libraries derived from natural-products are several types that have been reported.