Holistic drug design for multiparameter optimization in modern small molecule drug discovery

Toward structure-multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.

Total Synthesis as Training for Medicinal Chemistry

There is an ongoing debate about the best types of training in academia for practicing modern medicinal chemistry in the pharmaceutical and biotechnology industries of today. A case is made in this Viewpoint for the ongoing, and perhaps increasing, value of total synthesis as training for medicinal chemistry.

Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects

Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.

Towards holistic Compound Quality Scores: Extending ligand efficiency indices with compound pharmacokinetic characteristics

The suitability of small molecules as oral drugs is often assessed by simple physicochemical rules, the application of ligand efficiency scores or by composite scores based on physicochemical compound properties. These rules and scores are empirical and typically lack mechanistic background, such as information on pharmacokinetics (PK). We introduce new types of Compound Quality Scores (CQS, specifically called dose scores and cmax scores), which explicitly include predicted or, when available, experimental PK parameters and combine these with on-target potency. These CQS scores are surrogates for an estimated dose and corresponding cmax and allow prioritizing of compounds within test cascades as well as before synthesis. We demonstrate the complementarity and, in most cases, superior performance relative to existing efficiency metrics by project examples.

New drug approvals for 2022: Synthesis and clinical applications

The U.S. Food and Drug Administration has approved a total of 37 new drugs in 2022, which are composed of 20 chemical entities and 17 biologics. In particular, 20 chemical entities, including 17 small molecule drugs, 1 radiotherapy, and 2 diagnostic agents, provide privileged scaffolds, breakthrough clinical benefits, and a new mechanism of action for the discovery of more potent clinical candidates. The structure-based drug development with clear targets and fragment-based drug development with privileged scaffolds have always been the important modules in the field of drug discovery, which could easily bypass the patent protection and bring about improved biological activity. Therefore, we summarized the relevant valuable information about clinical application, mechanism of action, and chemical synthesis of 17 newly approved small molecule drugs in 2022. We hope this timely and comprehensive review could bring about creative and elegant inspiration on the synthetic methodologies and mechanism of action for the discovery of new drugs with novel chemical scaffolds and extended clinical indications.

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.

Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery

Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.

Applications of Isosteres of Piperazine in the Design of Biologically Active Compounds: Part 2

Applications of piperazine and homopiperazine in drug design are well-established, and these heterocycles have found use as both scaffolding and terminal elements and also as a means of introducing a water-solubilizing element into a molecule. In the accompanying review (10.1021/acs.jafc.2c00726), we summarized applications of piperazine and homopiperazine and their fused ring homologues in bioactive compound design along with illustrations of the use of 4-substituted piperidines and a sulfoximine-based mimetic. In this review, we discuss applications of pyrrolidine- and fused-pyrrolidine-based mimetics of piperazine and homopiperazine and illustrate derivatives of azetidine that include stretched and spirocyclic motifs, along with applications of a series of diaminocycloalkanes.

In Silico Positional Analogue Scanning with Amber GPU-TI

Positional analogue scanning (PAS) is an accepted strategy for multiparameter lead optimization (MPO) in drug discovery. Small structural changes as introduced by PAS can lead to 10-fold changes in binding potency in ∼10-20% of cases, a significant parameter shift irrespective of other MPO objectives. Sometimes performing a complete PAS is challenging due to resource and time constraints, building block availability, or difficulty in synthesis. Calculating relative binding free energies (RBFEs) for all positions can contribute to prioritizing the most promising analogues for synthesis. We tested a well-established RBFE calculation method, Amber GPU-TI, for 20 positional analogue scans in 14 test systems (cyclin-dependent kinase 8 (CDK8), hepatitis C virus nonstructural protein 5B (HCV NS5B), tankyrase, RAC-α serine/threonine-protein kinase (Akt), phosphodiesterase 1B (PDE1B), orexin/hypocretin receptor type 1 (OX1R), orexin/hypocretin receptor type 2 (OX2R), histone acetyltransferase K (lysine) acetyltransferase 6A (KAT6A), peroxisome proliferator-activated receptor γ (PPARγ), extracellular signal-regulated kinases (ERK1/2), coactivator-associated arginine methyltransferase 1 (PRMT4), α_vβ₆, bromodomain 1 (BD1), human immunodeficiency virus-1 (HIV-1) entry) involving nitrogen, methyl, halogen, methoxy, and hydroxyl scans with at least four analogues per set. Among the 66 analogue positions explored, we found that in 18 cases Amber GPU-TI calculations predicted a more than 10-fold change in potency. In all of these cases, the experimentally observed direction of potency changes agreed with the predictions. In 16 cases, more than 10-fold changes in experimental potency were observed. Again, in all of these cases, Amber GPU-TI predicted the direction of the potency changes correctly. In none of these cases would a decision made for or against synthesis based on a 10-fold change in potency have resulted in missing an important analogue. Therefore, in silico RBFE calculations using Amber GPU-TI can meaningfully contribute to the prioritization of positional analogues before synthesis.

Development of Novel Anti-Leishmanials: The Case for Structure-Based Approaches

The neglected tropical disease (NTD) leishmaniasis is the collective name given to a diverse group of illnesses caused by ~20 species belonging to the genus Leishmania, a majority of which are vector borne and associated with complex life cycles that cause immense health, social, and economic burdens locally, but individually are not a major global health priority. Therapeutic approaches against leishmaniasis have various inadequacies including drug resistance and a lack of effective control and eradication of the disease spread. Therefore, the development of a rationale-driven, target based approaches towards novel therapeutics against leishmaniasis is an emergent need. The utilization of Artificial Intelligence/Machine Learning methods, which have made significant advances in drug discovery applications, would benefit the discovery process. In this review, following a summary of the disease epidemiology and available therapies, we consider three important leishmanial metabolic pathways that can be attractive targets for a structure-based drug discovery approach towards the development of novel anti-leishmanials. The folate biosynthesis pathway is critical, as Leishmania is auxotrophic for folates that are essential in many metabolic pathways. Leishmania can not synthesize purines de novo, and salvage them from the host, making the purine salvage pathway an attractive target for novel therapeutics. Leishmania also possesses an organelle glycosome, evolutionarily related to peroxisomes of higher eukaryotes, which is essential for the survival of the parasite. Research towards therapeutics is underway against enzymes from the first two pathways, while the third is as yet unexplored.

The Selenoprotein Glutathione Peroxidase 4: From Molecular Mechanisms to Novel Therapeutic Opportunities

The selenoprotein glutathione peroxidase 4 (GPX4) is one of the main antioxidant mediators in the human body. Its central function involves the reduction of complex hydroperoxides into their respective alcohols often using reduced Glutathione (GSH) as a reducing agent. GPX4 has become a hotspot therapeutic target in biomedical research following its characterization as a chief regulator of ferroptosis, and its subsequent recognition as a specific pharmacological target for the treatment of an extensive variety of human diseases including cancers and neurodegenerative disorders. Several recent studies have provided insights into how GPX4 is distinguished from the rest of the glutathione peroxidase family, the unique biochemical properties of GPX4, how GPX4 is related to lipid peroxidation and ferroptosis, and how the enzyme may be modulated as a potential therapeutic target. This current report aims to review the literature underlying all these insights and present an up-to-date perspective on the current understanding of GPX4 as a potential therapeutic target.

From computer-aided drug discovery to computer-driven drug discovery

Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discovery process, but were not commonly regarded as a driving force in small molecule drug discovery. In the last decade however, there have been dramatic advances in the field, including (1) development of physics-based computational approaches to accurately predict a broad variety of endpoints from potency to solubility, (2) improvements in artificial intelligence and deep learning methods and (3) dramatic increases in computational power with the advent of GPUs and cloud computing, resulting in the ability to explore and accurately profile vast amounts of drug-like chemical space in silico. There have also been simultaneous advancements in structural biology such as cryogenic electron microscopy (cryo-EM) and computational protein-structure prediction, allowing for access to many more high-resolution 3D structures of novel drug-receptor complexes. The convergence of these breakthroughs has positioned structurally-enabled computational methods to be a driving force behind the discovery of novel small molecule therapeutics. This review will give a broad overview of the synergies in recent advances in the fields of computational chemistry, machine learning and structural biology, in particular in the areas of hit identification, hit-to-lead, and lead optimization.

Bioisosteres of the Phenyl Ring: Recent Strategic Applications in Lead Optimization and Drug Design

The benzene moiety is the most prevalent ring system in marketed drugs, underscoring its historic popularity in drug design either as a pharmacophore or as a scaffold that projects pharmacophoric elements. However, introspective analyses of medicinal chemistry practices at the beginning of the 21st century highlighted the indiscriminate deployment of phenyl rings as an important contributor to the poor physicochemical properties of advanced molecules, which limited their prospects of being developed into effective drugs. This Perspective deliberates on the design and applications of bioisosteric replacements for a phenyl ring that have provided practical solutions to a range of developability problems frequently encountered in lead optimization campaigns. While the effect of phenyl ring replacements on compound properties is contextual in nature, bioisosteric substitution can lead to enhanced potency, solubility, and metabolic stability while reducing lipophilicity, plasma protein binding, phospholipidosis potential, and inhibition of cytochrome P450 enzymes and the hERG channel.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.