Hit discovery and hit-to-lead approaches

A comprehensive review of small molecule drugs approved by the FDA in 2023: Advances and prospects

In 2023, the U.S. Food and Drug Administration has approved 55 novel medications, consisting of 17 biologics license applications and 38 new molecular entities. Although the biologics license applications including antibody and enzyme replacement therapy set a historical record, the new molecular entities comprising small molecule drugs, diagnostic agent, RNA interference therapy and biomacromolecular peptide still account for over 50 % of the newly approved medications. The novel and privileged scaffolds derived from drugs, active molecules and natural products are consistently associated with the discovery of new mechanisms, the expansion of clinical indications and the reduction of side effects. Moreover, the structural modifications based on the promising scaffolds can provide the clinical candidates with the improved biological activities, bypass the patent protection and greatly shorten the period of new drug discovery. Therefore, conducting an appraisal of drug approval experience and related information will expedite the identification of more potent drug molecules. In this review, we comprehensively summarized the pertinent information encompassing the clinical application, mechanism, elegant design and development processes of 28 small molecule drugs, and expected to provide the promising structural basis and design inspiration for pharmaceutical chemists.

The mechanism of action and chemical synthesis of FDA newly approved drug molecules

In 2023, the U.S. Food and Drug Administration has approved 29 small molecule drugs. These newly approved small molecule drugs possess the distinct scaffolds, thereby exhibiting diverse mechanisms of action and binding modalities. Moreover, the marketed drugs have always been an important source of new drug development and creative inspiration, thereby fostering analogous endeavors in drug discovery that potentially extend to the diverse clinical indications. Therefore, conducting a comprehensive evaluation of drug approval experience and associated information will facilitate the expedited identification of highly potent drug molecules. In this review, we comprehensively summarized the relevant information regarding the clinical applications, mechanisms of action and chemical synthesis of 29 small molecule drugs, with the aim of providing a promising structural basis and design inspiration for pharmaceutical chemists.

Encoding and display technologies for combinatorial libraries in drug discovery: The coming of age from biology to therapy

Drug discovery is a sophisticated process that incorporates scientific innovations and cutting-edge technologies. Compared to traditional bioactivity-based screening methods, encoding and display technologies for combinatorial libraries have recently advanced from proof-of-principle experiments to promising tools for pharmaceutical hit discovery due to their high screening efficiency, throughput, and resource minimization. This review systematically summarizes the development history, typology, and prospective applications of encoding and displayed technologies, including phage display, ribosomal display, mRNA display, yeast cell display, one-bead one-compound, DNA-encoded, peptide nucleic acid-encoded, and new peptide-encoded technologies, and examples of preclinical and clinical translation. We discuss the progress of novel targeted therapeutic agents, covering a spectrum from small-molecule inhibitors and nonpeptidic macrocycles to linear, monocyclic, and bicyclic peptides, in addition to antibodies. We also address the pending challenges and future prospects of drug discovery, including the size of screening libraries, advantages and disadvantages of the technology, clinical translational potential, and market space. This review is intended to establish a comprehensive high-throughput drug discovery strategy for scientific researchers and clinical drug developers.

bbSelect - An Open-Source Tool for Performing a 3D Pharmacophore-Driven Diverse Selection of R-Groups

The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size. An evaluation of bbSelect against established methods exemplified the superiority of bbSelect in its ability to perform diverse selections, achieving high levels of pharmacophore feature placement coverage with selection sizes of a fraction of the total set and without the introduction of excess complexity. bbSelect also reports visualizations and rationale to enable users to understand and interrogate results. This provides a tool for the drug discovery community to guide their hit-to-lead activities.

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

Discovery and structural optimization of a new series of N-acyl-2-aminobenzothiazole as inhibitors of Zika virus

Zika virus infection is associated to severe diseases such as congenital microcephaly and Zika fever causing serious harm to humans and special concern to health systems in low-income countries. Currently, there are no approved drugs against the virus, and the development of anti-Zika virus drugs is thus urgent. The present investigation describes the discovery and hit expansion of a N-acyl-2-aminobenzothiazole series of compounds against Zika virus replication. A structure-activity relationship study was obtained with the synthesis and evaluation of anti-Zika virus activity and cytotoxicity on Vero cells of nineteen derivatives. The three optimized compounds were 2.2-fold more potent than the initial hit and 20.9, 7.7 and 6.4-fold more selective. Subsequent phenotypic and biochemical assays were performed to evidence whether non-structural proteins, such as the complex NS2B-NS3pro, are related to the mechanism of action of the most active compounds.

Validation of a New Methodology to Create Oral Drugs beyond the Rule of 5 for Intracellular Tough Targets

Establishing a technological platform for creating clinical compounds inhibiting intracellular protein-protein interactions (PPIs) can open the door to many valuable drugs. Although small molecules and antibodies are mainstream modalities, they are not suitable for a target protein that lacks a deep cavity for a small molecule to bind or a protein found in intracellular space out of an antibody's reach. One possible approach to access these targets is to utilize so-called middle-size cyclic peptides (defined here as those with a molecular weight of 1000-2000 g/mol). In this study, we validated a new methodology to create oral drugs beyond the rule of 5 for intracellular tough targets by elucidating structural features and physicochemical properties for drug-like cyclic peptides and developing library technologies to afford highly N-alkylated cyclic peptide hits. We discovered a KRAS inhibitory clinical compound (LUNA18) as the first example of our platform technology.

How to Compute Atomistic Insight in DFT Clusters: The REG-IQA Approach

The relative energy gradient (REG) method is paired with the topological energy partitioning method interacting quantum atoms (IQA), as REG-IQA, to provide detailed and unbiased knowledge on the intra- and interatomic interactions. REG operates on a sequence of geometries representing a dynamical change of a system. Its recent application to peptide hydrolysis of the human immunodeficiency virus-1 (HIV-1) protease (PDB code: 4HVP) has demonstrated its full potential in recovering reaction mechanisms and through-space electrostatic and exchange-correlation effects, making it a compelling tool for analyzing enzymatic reactions. In this study, the computational efficiency of the REG-IQA method for the 133-atom HIV-1 protease quantum mechanical system is analyzed in every detail and substantially improved by means of three different approaches. The first approach of smaller integration grids for IQA integrations reduces the computational overhead by about a factor of 3. The second approach uses the line-simplification Ramer-Douglas-Peucker (RDP) algorithm, which outputs the minimal number of geometries necessary for the REG-IQA analysis for a predetermined root mean squared error (RMSE) tolerance. This cuts the computational time of the whole REG analysis by a factor of 2 if an RMSE of 0.5 kJ/mol is considered. The third approach consists of a "biased" or "unbiased" selection of a specific subset of atoms of the whole initial quantum mechanical model wave-function, which results in more than a 10-fold speed-up per geometry for the IQA calculation, without deterioration of the outcome of the REG-IQA analysis. Finally, to show the capability of these approaches, the findings gathered from the HIV-1 protease system are also applied to a different system named haloalcohol dehalogenase (HheC). In summary, this study takes the REG-IQA method to a computationally feasible and highly accurate level, making it viable for the analysis of a multitude of enzymatic systems.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction

Graph neural networks and convolutional architectures have proven to be pivotal in improving the prediction of molecular properties in drug discovery. However, this is fundamentally a low data problem that is incompatible with regular deep learning approaches. Contemporary deep networks require large amounts of training data, which severely limits the prediction of new molecular entities from limited available data. In this paper, we address the challenge of low data in molecular property prediction by: (1) defining a set of deep learning architectures that accept compound chemical structures in the form of molecular graphs, (2) creating a few-shot learning strategy across graph neural networks and convolutional neural networks to leverage the rich information of graph embeddings, and (3) proposing a two-module meta-learning framework to learn from task-transferable knowledge and predict molecular properties on few-shot data. Furthermore, we conduct multiple experiments on two benchmark multiproperty datasets to demonstrate a superior performance over conventional graph-based baselines. ROC-AUC results for 10-shot experiments show an average improvement of $$+11.37\%$$ + 11.37 % on Tox21 and $$+0.53\%$$ + 0.53 % on SIDER, which are representative small-sized biological datasets for molecular property prediction.

Label-free affinity screening, design and synthesis of inhibitors targeting the Mycobacterium tuberculosis L-alanine dehydrogenase

The ability of Mycobacterium tuberculosis (Mtb) to persist in its host may enable an evolutionary advantage for drug resistant variants to emerge. A potential strategy to prevent persistence and gain drug efficacy is to directly target the activity of enzymes that are crucial for persistence. We present a method for expedited discovery and structure-based design of lead compounds by targeting the hypoxia-associated enzyme L-alanine dehydrogenase (AlaDH). Biochemical and structural analyses of AlaDH confirmed binding of nucleoside derivatives and showed a site adjacent to the nucleoside binding pocket that can confer specificity to putative inhibitors. Using a combination of dye-ligand affinity chromatography, enzyme kinetics and protein crystallographic studies, we show the development and validation of drug prototypes. Crystal structures of AlaDH-inhibitor complexes with variations at the N6 position of the adenyl-moiety of the inhibitor provide insight into the molecular basis for the specificity of these compounds. We describe a drug-designing pipeline that aims to block Mtb to proliferate upon re-oxygenation by specifically blocking NAD accessibility to AlaDH. The collective approach to drug discovery was further evaluated through in silico analyses providing additional insight into an efficient drug development strategy that can be further assessed with the incorporation of in vivo studies.

Identification of CB1 Ligands among Drugs, Phytochemicals and Natural-Like Compounds: Virtual Screening and In Vitro Verification

Cannabinoid receptor type 1 (CB1) is an important modulator of many key physiological functions and thus a compelling molecular target. However, safe CB1 targeting is a non-trivial task. In recent years, there has been a surge of data indicating that drugs successfully used in the clinic for years (e.g. paracetamol) show CB1 activity. Moreover, there is a lot of promise in finding CB1 ligands in plants other than Cannabis sativa. In this study, we searched for possible CB1 activity among already existing drugs, their metabolites, phytochemicals, and natural-like molecules. We conducted two iterations of virtual screening, verifying the results with in vitro binding and functional assays. The in silico procedure consisted of a wide range of structure- and ligand-based methods, including docking, molecular dynamics, and quantitative structure-activity relationship (QSAR). As a result, we identified travoprost and ginkgetin as CB1 ligands, which provides a starting point for future research on the impact of their metabolites or preparations on the endocannabinoid system. Moreover, we found five natural-like compounds with submicromolar or low micromolar affinity to CB1, including one mixed partial agonist/antagonist viable for hit-to-lead phase. Finally, the computational procedure established in this work will be of use for future screening campaigns for novel CB1 ligands.

Diazabicyclo derivatives as safeners protect cotton from injury caused by flumioxazin

Flumioxazin, a protoporphyrinogen oxidase (PPO; EC 1.3.3.4) inhibitor, has been used in soybean, cotton, grapes, and many other crops to control broad leaf weeds. Unfortunately, it can cause damage to cotton. To ameliorate phytotoxicity of flumioxazin to cotton, this work assessed the protective effects of diazabicyclo derivatives as potential safeners in cotton. A bioactivity assay proved that the phytotoxicity of flumioxazin on cotton was alleviated by some of the compounds. In particular, the activity of glutathione S-transferases (GSTs) was significantly enhanced by Compound 32, which showed good safening activity against flumioxazin injury. The physicochemical properties and absorption, distribution, metabolism, excretion and toxicity (ADMET) predictions proved that the pharmacokinetic properties of Compound 32 are similar to those of the commercial safener BAS 145138. The present work demonstrated that diazabicyclo derivatives are potentially efficacious as herbicide safeners, meriting further investigation.

Pocket-to-Lead: Structure-Based De Novo Design of Novel Non-peptidic HIV-1 Protease Inhibitors Using the Ligand Binding Pocket as a Template

A novel strategy for lead identification that we have dubbed the "Pocket-to-Lead" strategy is demonstrated using HIV-1 protease as a model target. Sometimes, it is difficult to obtain hit compounds because of the difficulties in satisfying the complex pharmacophoric features. In this study, a virtual fragment hit which does not match all of the pharmacophore features but has key interactions and vectors that could grow into remaining pharmacophore features was optimized in silico. The designed compound 9 demonstrated weak but evident inhibitory activity (IC₅₀ = 54 μM), and the design concept was proven by the co-crystal structure. Then, structure-based drug design promptly gave compound 14 (IC₅₀ = 0.0071 μM, EC₅₀ = 0.86 μM), an almost 10,000-fold improvement in activity from 9. The structure of the designed molecules proved to be novel with high synthetic feasibility, indicating the usefulness of this strategy to tackle tough targets with complex pharmacophore.

Screening for Inhibitors of Main Protease in SARS-CoV-2: In Silico and In Vitro Approach Avoiding Peptidyl Secondary Amides

In addition to vaccines, antiviral drugs are essential for suppressing COVID-19. Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space, excluding peptidyl secondary amides. In total, 180 compounds were subjected to the primary assay at 20 μM, and nine compounds with inhibition rates of >5% were obtained. The IC50 of six compounds was determined in dose-response experiments, with the values on the order of 10-4 M. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Cytotoxic and antiproliferative activity of thiazole derivatives of Ochraceolide A

A series of 15 novel 1,3-thiazole amide derivatives of the pentacyclic triterpene Ochraceolide A (1) was synthesized, characterized, and evaluated in vitro against three human cancer cell lines (MCF-7, MDA-MB-231 and SiHa) and a normal cell line (Vero). Synthetic derivatives were obtained by acylation of the 2-aminothiazole triterpene 2, previously reported. Remarkably, the 5-nitrofuramide derivative (2o) showed better cytotoxic and antiproliferative activity than compound 2 and the other derivatives against the three cancer cell lines with CC₅₀ and IC₅₀ values of 1.6-12.7 µM. Furthermore, butyramide derivative (2c) was approximately 25 times more selective than 2, as well as 3.4 times more selective than Docetaxel, against SiHa cells in the cytotoxic assay, while the phenyl amide derivatives were inactive against the three cancer cell lines.

Quinoxaline derivatives as herbicide safeners by improving Zea mays tolerance

Isoxaflutole (IXF), a 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor, causes injury to crops leading to reductions in grain yield. In order to solve the phytotoxicity caused by IXF, the present work evaluated the protective response of the substituted quinoxaline derivatives as potential safeners on Zea mays. The bioassay results showed that all of the test compounds displayed protection against IXF. In particular, safener I-6 exhibited excellent safener activity against IXF injury via enhancing glutathione (GSH) content, glutathione S transferases (GSTs) and cytochrome P450 monooxygenases (CYP450) activity. The tested compounds induced the activity of CYP450 and GSTs in Z. mays. The physicochemical properties and ADMET properties of safener I-6, benoxacor and diketonitrile (DKN, IXF metabolite) were compared to predict pharmaceutical behavior. The present work demonstrates that the safener I-6 could be considered as a potential candidate for developing novel safeners in the future.

Optimization and benchmarking of the thermal cycling algorithm

Optimization plays a significant role in many areas of science and technology. Most of the industrial optimization problems have inordinately complex structures that render finding their global minima a daunting task. Therefore, designing heuristics that can efficiently solve such problems is of utmost importance. In this paper we benchmark and improve the thermal cycling algorithm [Phys. Rev. Lett. 79, 4297 (1997)PRLTAO0031-900710.1103/PhysRevLett.79.4297] that is designed to overcome energy barriers in nonconvex optimization problems by temperature cycling of a pool of candidate solutions. We perform a comprehensive parameter tuning of the algorithm and demonstrate that it competes closely with other state-of-the-art algorithms such as parallel tempering with isoenergetic cluster moves, while overwhelmingly outperforming more simplistic heuristics such as simulated annealing.

A skills framework integrating professionally relevant medicinal chemistry proficiencies to strengthen the contemporary practice of pharmacy

OBJECTIVE

Our aim was to define a repository of competences in Medicinal Chemistry, to be developed during Pharmacy studies and expected in professional practices, while highlighting the fundamental character of the subject and its interdisciplinary links within the Pharmaceutical Sciences.

METHODS

A first version, based on both our professional and educational experience, consolidated by a review of educational articles and good practice guidelines, was obtained by following a competency-based approach. It was then completed by Medicinal Chemistry teachers in various French Pharmacy Faculties to obtain a comprehensive data set. The final version was reviewed in the light of relevant comments from 15 experts from related disciplines.

RESULTS

A comprehensive competency framework with extensive practical applications was developed.

CONCLUSIONS

This pilot study provides a teaching repository for medicinal chemistry for use by teachers of medicinal sciences. It highlights the fundamental role of the discipline within Pharmacy studies and provides links with professional practices. This repository will be useful to various teaching teams in a context of integrated disciplines and could be replicated in related disciplines.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Into the Fray! A Beginner's Guide to Medicinal Chemistry

Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.

Fragment Binding to Kinase Hinge: If Charge Distribution and Local pKa Shifts Mislead Popular Bioisosterism Concepts

Medicinal-chemistry optimization follows strategies replacing functional groups and attaching larger substituents at a promising lead scaffold. Well-established bioisosterism rules are considered, however, it is difficult to estimate whether the introduced modifications really match the required properties at a binding site. The electron density distribution and pKa values are modulated influencing protonation states and bioavailability. Considering the adjacent H-bond donor/acceptor pattern of the hinge binding motif in a kinase, we studied by crystallography a set of fragments to map the required interaction pattern. Unexpectedly, benzoic acid and benzamidine, decorated with the correct substituents, are totally bioisosteric just as carboxamide and phenolic OH. A mono-dentate pyridine nitrogen out-performs bi-dentate functionalities. The importance of correctly designing pKa values of attached functional groups by additional substituents at the parent scaffold is rendered prominent.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Chemical Screening of Nuclear Receptor Modulators

Nuclear receptors are ligand-inducible transcriptional factors that control multiple biological phenomena, including proliferation, differentiation, reproduction, metabolism, and the maintenance of homeostasis. Members of the nuclear receptor superfamily have marked structural and functional similarities, and their domain functionalities and regulatory mechanisms have been well studied. Various modulators of nuclear receptors, including agonists and antagonists, have been developed as tools for elucidating nuclear receptor functions and also as drug candidates or lead compounds. Many assay systems are currently available to evaluate the modulation of nuclear receptor functions, and are useful as screening tools in the discovery and development of new modulators. In this review, we cover the chemical screening methods for nuclear receptor modulators, focusing on assay methods and chemical libraries for screening. We include some recent examples of the discovery of nuclear receptor modulators.

Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead

Motivated by the growing demand for reducing the chemical optimization burden of H2L, we developed auto in silico ligand directing evolution (AILDE, http://chemyang.ccnu.edu.cn/ccb/server/AILDE), an efficient and general approach for the rapid identification of drug leads in accessible chemical space. This computational strategy relies on minor chemical modifications on the scaffold of a hit compound, and it is primarily intended for identifying new lead compounds with minimal losses or, in some cases, even increases in ligand efficiency. We also described how AILDE greatly reduces the chemical optimization burden in the design of mesenchymal-epithelial transition factor (c-Met) kinase inhibitors. We only synthesized eight compounds and found highly efficient compound 5g, which showed an ∼1,000-fold improvement in in vitro activity compared with the hit compound. 5g also displayed excellent in vivo antitumor efficacy as a drug lead. We believe that AILDE may be applied to a large number of studies for rapid design and identification of drug leads.

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AILDE was developed for the rapid identification of drug leads

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A potent drug lead targeted to c-Met was found by synthesizing only eight compounds

Lead Identification of 8-(Methylamino)-2-oxo-1,2-dihydroquinoline Derivatives as DNA Gyrase Inhibitors: Hit-to-Lead Generation Involving Thermodynamic Evaluation

DNA gyrase and topoisomerase IV are well-validated pharmacological targets, and quinolone antibacterial drugs are marketed as their representative inhibitors. However, in recent years, resistance to these existing drugs has become a problem, and new chemical classes of antibiotics that can combat resistant strains of bacteria are strongly needed. In this study, we applied our hit-to-lead (H2L) chemistry for the identification of a new chemical class of GyrB/ParE inhibitors by efficient use of thermodynamic parameters. Investigation of the core fragments obtained by fragmentation of high-throughput screening hit compounds and subsequent expansion of the hit fragment was performed using isothermal titration calorimetry (ITC). The 8-(methylamino)-2-oxo-1,2-dihydroquinoline derivative 13e showed potent activity against Escherichia coli DNA gyrase with an IC₅₀ value of 0.0017 μM. In this study, we demonstrated the use of ITC for primary fragment screening, followed by structural optimization to obtain lead compounds, which advanced into further optimization for creating novel antibacterial agents.

Identifying Ortholog Selective Fragment Molecules for Bacterial Glutaredoxins by NMR and Affinity Enhancement by Modification with an Acrylamide Warhead

Illustrated here is the development of a new class of antibiotic lead molecules targeted at Pseudomonas aeruginosa glutaredoxin (PaGRX). This lead was produced to (a) circumvent efflux-mediated resistance mechanisms via covalent inhibition while (b) taking advantage of species selectivity to target a fundamental metabolic pathway. This work involved four components: a novel workflow for generating protein specific fragment hits via independent nuclear magnetic resonance (NMR) measurements, NMR-based modeling of the target protein structure, NMR guided docking of hits, and synthetic modification of the fragment hit with a vinyl cysteine trap moiety, i.e., acrylamide warhead, to generate the chimeric lead. Reactivity of the top warhead-fragment lead suggests that the ortholog selectivity observed for a fragment hit can translate into a substantial kinetic advantage in the mature warhead lead, which bodes well for future work to identify potent, species specific drug molecules targeted against proteins heretofore deemed undruggable.

Structural simplification: an efficient strategy in lead optimization

The trend toward designing large hydrophobic molecules for lead optimization is often associated with poor drug-likeness and high attrition rates in drug discovery and development. Structural simplification is a powerful strategy for improving the efficiency and success rate of drug design by avoiding "molecular obesity". The structural simplification of large or complex lead compounds by truncating unnecessary groups can not only improve their synthetic accessibility but also improve their pharmacokinetic profiles, reduce side effects and so on. This review will summarize the application of structural simplification in lead optimization. Numerous case studies, particularly those involving successful examples leading to marketed drugs or drug-like candidates, will be introduced and analyzed to illustrate the design strategies and guidelines for structural simplification.

Using Physicochemical Measurements to Influence Better Compound Design

During the past decade, the physicochemical quality of molecules under investigation at all stages of the drug discovery process has come under particular scrutiny. The issues associated with excessive lipophilicity and poor solubility in particular are many and varied, ranging from poor outcomes in screening campaigns to promiscuity, limited and/or poorly predictable pharmacokinetic exposure, and, ultimately, greater chances of clinical failure. In this review, contemporary methods to secure key measurements are described along with their relevance to understanding the behavior of molecules in environments pertinent to pharmacological activity. Together, the various measurements contribute to predictive models of both the physicochemical properties themselves and the outcomes they influence.

Development and rigorous validation of antimalarial predictive models using machine learning approaches

The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against Plasmodium falciparum. Four different machine learning methods, namely the support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN) and XGBoost have been used for the development of models using the diverse antimalarial dataset from ChEMBL. A well-established feature selection framework was used to select the best subset from a larger pool of descriptors. Performance of the models was rigorously evaluated by evaluation of the applicability domain, Y-scrambling and AUC-ROC curve. Additionally, the predictive power of the models was also assessed using probability calibration and predictiveness curves. SVM and XGBoost showed the best performances, yielding an accuracy of ~85% on the independent test set. In term of probability prediction, SVM and XGBoost were well calibrated. Total gain (TG) from the predictiveness curve was more related to SVM (TG = 0.67) and XGBoost (TG = 0.75). These models also predict the high-affinity compounds from PubChem antimalarial bioassay (as external validation) with a high probability score. Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery of antimalarial agents.

A Structure-Based Drug Discovery Paradigm

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Optimization of novel reversible Bruton's tyrosine kinase inhibitors identified using Tethering-fragment-based screens

Since the approval of ibrutinib for the treatment of B-cell malignancies in 2012, numerous clinical trials have been reported using covalent inhibitors to target Bruton's tyrosine kinase (BTK) for oncology indications. However, a formidable challenge for the pharmaceutical industry has been the identification of reversible, selective, potent molecules for inhibition of BTK. Herein, we report application of Tethering-fragment-based screens to identify low molecular weight fragments which were further optimized to improve on-target potency and ADME properties leading to the discovery of reversible, selective, potent BTK inhibitors suitable for pre-clinical proof-of-concept studies.

Inhibiting two cellular mutant epidermal growth factor receptor tyrosine kinases by addressing computationally assessed crystal ligand pockets

Aim: Blocking receptor tyrosine kinases is a useful strategy for inhibiting the overexpression of EGFR. However, the quality of crystal pocket is an essential issue for virtually identifying new leads for surviving resistance cancer cells. Results: With the examinating crystal pocket quality by the self-docking root-mean-square deviation (RMSD) calculation, we used the two best kinase pockets of mutant EGFR kinases, T790M/L858R and G719S, for virtual screening. After sorting all the docking poses of the 57,177 library compounds by consensus scores, three evidently blocked cellular EGFR phosphorylation in the H1975 and SW48 cell lines. Conclusion: The computationally assessed qualities of crystal pockets of crystal EGFR kinases can help identify new cellular active and target-specific ligands rapidly and at low cost.

Antiproliferative Activity of Extracts of Campomanesia adamantium (Cambess.) O. Berg and Isolated Compound Dimethylchalcone Against B16-F10 Murine Melanoma

Campomanesia adamantium, a native species of the Brazilian Cerrado, is characterized as a natural source of phenolic compounds and has known potential anticancer activities. This study aimed to evaluate the chemical profile of dichloromethane extracts of pulp (DEGPU) and peel (DEGPE) from the fruits of C. adamantium and to identify compounds with antiproliferative effects in vitro against melanoma cells by sulforhodamine B (SRB) assay, apoptosis induction assay, caspase-3 activation assay, nitric oxide (NO) release in coculture of B16-F10 cells and murine peritoneal macrophages. The chemical profiles of DEGPU and DEGPE were analyzed by high performance liquid chromatography coupled to diode array detector and mass spectrometer using the electrospray ionization interface (HPLC-DAD-ESI-MS/MS). Thirteen compounds were identified in both extracts and the chromatographic study of the most active extract in SRB assay DEGPU (GI₅₀ of 16.17 μg/mL) resulted in the isolation of seven compounds. The isolated compound dimethylchalcone (DMC) had the highest antiproliferative activity against B16-F10 with a GI₅₀ of 7.11 μg/mL. DEGPU extract activated caspase-3 in 29% of cells at 25 μg/mL and caused a 50% decrease in NO release in coculture. DEGPU can be characterized as a source of bioactive compounds such as DMC, as seen from its antiproliferative effect in vitro by inducing B16-F10 cells to undergo apoptosis, essential feature in the search for new anticancer drugs.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Antiproliferative Activity of Walnut (Juglans regia L.) Proteins and Walnut Protein Hydrolysates

Proteins from Juglans regia L. were isolated. Then, proteins were hydrolyzed with different enzymes. Antiproliferative activity of proteins and of the protein hydrolysates of J. regia L. were evaluated using the sulforhodamine B method. Glutelin and prolamin proteins presented a high antiproliferative activity against cancer cells PC-3 (prostate) and K-562 (leukemia) with values of 43.9 and 84.4 μg/mL, respectively. The highest inhibitory effect observed was 50% at 0.25 μg/mL concentration in gastrointestinal digestion with pepsin and corolase pp in a dose-dependent manner against cancer cell UACC62 (melanoma). Pepsin hydrolysate showed inhibitory effects against cancer cell UACC62 (melanoma) with a concentration of 71.0 μg/mL. The effects were studied in a dose-dependent manner. The hydrolysate obtained with neutrase enzyme presented inhibitory effects against cancer cell UACC62 (melanoma) at a concentration of 25 μg/mL. Neither proteins nor protein hydrolysates presented cytotoxicity against normal cell assay VERO (epithelial).

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

Potent immunomodulatory activity of a highly selective cannabinoid CB2 agonist on immune cells from healthy subjects and patients with multiple sclerosis

COR167, a novel CB2-selective high affinity agonist, was found to significantly inhibit, in a dose-dependent manner, the proliferation of both peripheral blood mononuclear cells and myelin basic protein-reactive T cell lines from normal healthy subjects and patients with relapsing-remitting multiple sclerosis (MS). In MS, a significantly higher inhibition was observed in patients on treatment with disease modifying drugs compared to those naive to treatment. The inhibitory activity of COR167 was exerted through a mixed mechanism involving atypical and incomplete shift of Th1 phenotype towards Th2 phenotype associated with slight reduction of IL-4 and IL-5 as well as strongly reduced levels of Th17-related cytokines. COR167 was also able to reduce in vitro migration of stimulated immunocompetent cells through human brain endothelium associated with a significant reduction of levels of several chemokines. These findings demonstrate that COR167 exerts potent immunomodulatory effects and confirm the cannabinoid CB2 receptor as a novel pharmacological target to counteract neuroinflammation.