Improved graph-based multitask learning model with sparse sharing for quantitative structure–property relationship prediction of drug molecules

The quantitative structure–property relationship (QSPR) is a fundamental technique for evaluating and screening potentially valuable molecules in the field of drug discovery. There is an urgent need to speed up pharmaceutical research and development and a huge chemical space to explore, which necessitate effective and precise computer-aided QSPR modeling methods. Previous studies with various deep learning models are limited because they are trained on separate small datasets, known as the small-sample problem. Using transfer learning, this article describes a sparse sharing method that uses advanced graph-based models to construct an efficient and reasonable multitask learning workflow for QSPR prediction. The proposed workflow is systematically and comprehensively tested with four benchmark datasets containing different targets, and several precisely predicted molecular examples are illustrated. The results demonstrate that an obvious improvement in the prediction of molecular properties is achieved, along with the ability to predict multiple properties simultaneously.     

Optimization of the Bacterial Cytochrome P450 BM3 System for the Production of Human Drug Metabolites

Drug metabolism in human liver is a process involving many different enzymes. Among them, a number of cytochromes P450 isoforms catalyze the oxidation of most of the drugs commercially available. Each P450 isoform acts on more than one drug, and one drug may be oxidized by more than one enzyme. As a result, multiple products may be obtained from the same drug, and as the metabolites can be biologically active and may cause adverse drug reactions (ADRs), the metabolic profile of a new drug has to be known before this can be commercialized. Therefore, the metabolites of a certain drug must be identified, synthesized and tested for toxicity. Their synthesis must be in sufficient quantities to be used for metabolic tests. This review focuses on the progresses done in the field of the optimization of a bacterial self-sufficient and efficient cytochrome P450, P450 BM3 from Bacillus megaterium, used for the production of metabolites of human enzymes. The progress made in the improvement of its catalytic performance towards drugs, the substitution of the costly NADPH cofactor and its immobilization and scale-up of the process for industrial application are reported.     

Activity-based probes for the study of proteases: recent advances and developments

Proteases are important targets for the treatment of human disease. Several protease inhibitors have failed in clinical trials due to a lack of in vivo specificity, indicating the need for studies of protease function and inhibition in complex, disease-related models. The tight post-translational regulation of protease activity complicates protease analysis by traditional proteomics methods. Activity-based protein profiling is a powerful technique that can resolve this issue. It uses small-molecule tools-activity-based probes-to label and analyze active enzymes in lysates, cells, and whole animals. Over the last twelve years, a wide variety of protease activity-based probes have been developed. These synthetic efforts have enabled techniques ranging from real-time in vivo imaging of protease activity to high-throughput screening of uncharacterized proteases. This Review introduces the general principles of activity-based protein profiling and describes the recent advancements in probe design and analysis techniques, which have increased the knowledge of protease biology and will aid future protease drug discovery.     

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.     

Glycosyltransferases and their Assays

Glycosyltransferases (GTs) are a large family of enzymes that are essential in all domains of life for the biosynthesis of complex carbohydrates and glycoconjugates. GTs catalyse the transfer of a sugar from a glycosyl donor to a variety of acceptor molecules, for example, oligosaccharides, peptides, lipids or small molecules. Such glycosylation reactions are central to many fundamental biological processes, including cellular adhesion, cell signalling and bacterial‐ and plant‐cell‐wall biosynthesis. GTs are therefore of significant interest as molecular targets in chemical biology and drug discovery. In addition, GTs have found wide application as synthetic tools for the preparation of complex carbohydrates and glycoconjugates. In order to exploit the potential of GTs both as molecular targets and synthetic tools, robust and operationally simple bioassays are essential, especially as more and more protein sequences with putative GT activity but unknown biochemical function are being identified. In this minireview, we give a brief introduction to GT biochemistry and biology. We outline the relevance of GTs for medicinal chemistry and chemical biology, and describe selected examples for recently developed GT bioassays, with a particular emphasis on fluorescence‐based formats.     

Fragment‐Based Phenotypic Lead Discovery: Cell‐Based Assay to Target Leishmaniasis

A rapid and practical approach for the discovery of new chemical matter for targeting pathogens and diseases is described. Fragment‐based phenotypic lead discovery (FPLD) combines aspects of traditional fragment‐based lead discovery (FBLD), which involves the screening of small‐molecule fragment libraries to target specific proteins, with phenotypic lead discovery (PLD), which typically involves the screening of drug‐like compounds in cell‐based assays. To enable FPLD, a diverse library of fragments was first designed, assembled, and curated. This library of soluble, low‐molecular‐weight compounds was then pooled to expedite screening. Axenic cultures of Leishmania promastigotes were screened, and single hits were then tested for leishmanicidal activity against intracellular amastigote forms in infected murine bone‐marrow‐derived macrophages without evidence of toxicity toward mammalian cells. These studies demonstrate that FPLD can be a rapid and effective means to discover hits that can serve as leads for further medicinal chemistry purposes or as tool compounds for identifying known or novel targets.     

Chemical Strategies for Visualizing and Analyzing Endogenous Nonribosomal Peptide Synthetase (NRPS) Megasynthetases

Nonribosomal peptide (NRP) natural products are among the most promising resources for drug discovery and development, owing to their wide range of biological activities and therapeutic applications. These peptide metabolites are biosynthesized by large multienzyme machinery known as NRP synthetases (NRPSs). The structural complexity of a number of NRPs poses an enormous challenge in their synthesis. A major issue in this field is reprogramming NRPS machineries to allow the biosynthetic production of artificial peptides. NRPS adenylation (A) domains are responsible for the incorporation of a wide variety of amino acids and can be considered as reprogramming sites; therefore, advanced methods to accelerate the functional prediction and assessment of A-domains are required. This Concept article demonstrates that activity-based protein profiling of NRPSs offers a simple, rapid, and robust analytical platform for A-domains and provides insights into enzyme–substrate candidates and active-site microenvironments. It also describes the background associated with the development and application of a method to analyze endogenous NRPS machinery in its natural environment.     

Chemical Translational Biology: Redefining Druggability of Protein-Protein Interactions

Chemical biologists use chemical tools to answer biological questions. The translational application of these principles has led to an explosion in the discovery and druggability of new protein targets, including protein-protein interactions (PPIs). Proteins tend to interact with other macromolecules using relatively large and featureless binding surfaces, which has hampered traditional drug discovery efforts, particularly for interactions with weaker affinity. In this article, I discuss several emerging strategies for targeting PPIs, including computational and structural methods and novel screening approaches. In particular, I focus on hijacking intrinsic protein allosteric pathways for the discovery and design of small-molecule and peptide ligands.     

D-Peptide-Based Drug Discovery Aided by Chemical Protein Synthesis

Despite a sharp increase in the expenditures for drug research and development (R&D) in the past decade, the declining trend in the number of new drugs approved annually by the US Food and Drug Administration continues. This growing disparity between R&D investment and new drug approvals results in part from the deficiency in promising therapeutic targets and leads to a stagnation exacerbated by the lack of advanced drug discovery tools for harvesting the “high-hanging fruits” such as inhibitors of protein–protein interactions (PPIs). Small peptide inhibitors of PPIs can be of high affinity and specificity, promising an important class of therapeutic agents that target PPIs involved in a great variety of biological processes. However, susceptibility to proteolytic degradation in vivo still remains a major hurdle that limits their therapeutic potential. This limitation can be overcome by mirror-image phage display, a technique that allows, through phage-expressed peptide library screening against the D -enantiomer of a target protein, for the identification of proteolysis-resistant D -peptide inhibitors of PPIs. Recent advances in total protein synthesis via native chemical ligation have significantly expanded the scope of molecular targets for mirror-image phage display. This concise review focuses on the latest development in the combined use of mirror-image phage display and native chemical ligation for D -peptide based anticancer drug discovery.     

The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.     

Chemical Proteomics for Expanding the Druggability of Human Disease

Abstract . Over the past decade, chemical proteomics has emerged as a powerful technique to understand small molecule and protein function in the physiological system and plays a key role in unravelling the cellular targets of pharmacological modulators. Chemical proteomics that integrates activity-based protein profiling (ABPP) with mass spectrometry has been introduced to evaluate small-molecule and protein interaction and expand the druggable proteome. A much larger fraction of the human proteome can now be targeted by small molecules than estimated by past predictions of protein druggability.     

Drug Discovery Alliances in India—Indications,Targets, and New Chemical Entities

Global pharmaceutical and biotechnology companies have been building increasingly on the skills and services offered by Indian biotech companies through strategic collaborative partnerships and alliances to fuel their in‐house discovery and development pipelines. With the exception of generic press releases, however, very little has been published on the process and progress of drug discovery itself, such as the targets or modes of action involved, nor on the scientific output of such collaborations, and therefore on new chemical entities coming out of India through research collaborations. This Essay provides an analytical review of recent patents, patent applications, and peer‐reviewed publications of major research alliances. It aims at highlighting their scientific output as well as the considerable bandwidth of targets and therapeutic areas involved.     

hiPSC-Derived Cardiac Tissue for Disease Modeling and Drug Discovery

Relevant, predictive normal, or disease model systems are of vital importance for drug development. The difference between nonhuman models and humans could contribute to clinical trial failures despite ideal nonhuman results. As a potential substitute for animal models, human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (CMs) provide a powerful tool for drug toxicity screening, modeling cardiovascular diseases, and drug discovery. Here, we review recent hiPSC-CM disease models and discuss the features of hiPSC-CMs, including subtype and maturation and the tissue engineering technologies for drug assessment. Updates from the international multisite collaborators/administrations for development of novel drug discovery paradigms are also summarized.     

Discovery of New Potential Anti‐Infective Compounds Based on Carbonic Anhydrase Inhibitors by Rational Target‐Focused Repurposing Approaches

In academia, compound recycling represents an alternative drug discovery strategy to identify new pharmaceutical targets from a library of chemical compounds available in house. Herein we report the application of a rational target‐based drug‐repurposing approach to find diverse applications for our in‐house collection of compounds. The carbonic anhydrase (CA, EC 4.2.1.1) metalloenzyme superfamily was identified as a potential target of our compounds. The combination of a thoroughly validated docking screening protocol, together with in vitro assays against various CA families and isoforms, allowed us to identify two unprecedented chemotypes as CA inhibitors. The identified compounds have the capacity to preferentially bind pathogenic (bacterial/protozoan) CAs over human isoforms and represent excellent hits for further optimization in hit‐to‐lead campaigns.     

Unpacking the Pathogen Box—An Open Source Tool for Fighting Neglected Tropical Disease

The Pathogen Box is a 400-strong collection of drug-like compounds, selected for their potential against several of the world's most important neglected tropical diseases, including trypanosomiasis, leishmaniasis, cryptosporidiosis, toxoplasmosis, filariasis, schistosomiasis, dengue virus and trichuriasis, in addition to malaria and tuberculosis. This library represents an ensemble of numerous successful drug discovery programmes from around the globe, aimed at providing a powerful resource to stimulate open source drug discovery for diseases threatening the most vulnerable communities in the world. This review seeks to provide an in-depth analysis of the literature pertaining to the compounds in the Pathogen Box, including structure–activity relationship highlights, mechanisms of action, related compounds with reported activity against different diseases, and, where appropriate, discussion on the known and putative targets of compounds, thereby providing context and increasing the accessibility of the Pathogen Box to the drug discovery community.     

Chemical strategies for activity-based proteomics

The assignment of molecular and cellular functions to the numerous protein products encoded by prokaryotic and eukaryotic genomes presents a major challenge for the field of proteomics. To address this problem, chemical approaches have been introduced that utilize small-molecule probes to profile dynamics in enzyme activity in complex proteomes. These strategies for activity-based protein profiling enable both the discovery and functional analysis of enzymes associated with human disease.     

Structure-Based Drug Discovery with Deep Learning**

Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep-learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.     

Using a Fragment‐Based Approach To Target Protein–Protein Interactions

The ability to identify inhibitors of protein–protein interactions represents a major challenge in modern drug discovery and in the development of tools for chemical biology. In recent years, fragment‐based approaches have emerged as a new methodology in drug discovery; however, few examples of small molecules that are active against chemotherapeutic targets have been published. Herein, we describe the fragment‐based approach of targeting the interaction between the tumour suppressor BRCA2 and the recombination enzyme RAD51; it makes use of a screening pipeline of biophysical techniques that we expect to be more generally applicable to similar targets. Disruption of this interaction in vivo is hypothesised to give rise to cellular hypersensitivity to radiation and genotoxic drugs. We have used protein engineering to create a monomeric form of RAD51 by humanising a thermostable archaeal orthologue, RadA, and used this protein for fragment screening. The initial fragment hits were thoroughly validated biophysically by isothermal titration calorimetry (ITC) and NMR techniques and observed by X‐ray crystallography to bind in a shallow surface pocket that is occupied in the native complex by the side chain of a phenylalanine from the conserved FxxA interaction motif found in BRCA2. This represents the first report of fragments or any small molecule binding at this protein–protein interaction site.     

A Consistent Dataset of Kinetic Solubilities for Early‐Phase Drug Discovery

Herein, we describe a new dataset of kinetic aqueous solubilities determined by nephelometry for 711 druglike compounds. The solubilities are reported in twelve classes ranging from <2 μg mL^?1 to >250 μg mL^?1. The measurements were designed to provide the appropriate data for applications in the early phases of drug discovery. Three class classification models (insoluble, moderately soluble, soluble) were built using the random forest algorithm and their performance for this dataset was analyzed.