Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

The discovery of readily available and easily modifiable new models is a crucial and practical solution for agrochemical innovation. Antifungal function-oriented fusion of triazole with the prevalidated lead (R)-LE001 affords a novel framework with a broad and enhanced antifungal spectrum. Characterized by the easy accessibility and adjustability of [1,2,4]triazolo[4,3-a]pyridine, modular fine-tuning provided a set of unprecedented leads (e.g., Z23, Z25, Z26, etc.) with superior antifungal potentials than the positive control boscalid. Candidate Z23 exhibited a more promising antifungal activity against Sclerotinia sclerotiorum, Botrytis cinerea, and Phytophthora capsici with EC50 values of 0.7, 0.6, and 0.5 μM, respectively. This candidate could effectively control boscalid-resistant B. cinerea strains and also exhibit good vivo efficacy in controlling gray mold. Noteworthily, both the SDH-inhibition and the efficiency against Oomycete P. capsici are quite distinct from that of the positive control boscalid. A molecular docking simulation also differentiates Z23 from boscalid. These findings highlight the potential of [1,2,4]triazolo[4,3-a]pyridine amide as a novel antifungal model.

Academic and other non-profit institutions have a long-term vision to improve human health where commercial interests can be limited for profit organizations. Medicinal chemistry to these diseases with no commercial benefit needs is well suited in the academic environment and this chapter outlines some work conducted at Calibr-Skaggs around antibiotic drug development that has led to initiation of multiple clinical trials over the last decade.

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In previous studies, we developed anti-trypanosome tubulin inhibitors with promising in vitro selectivity and activity against Human African Trypanosomiasis (HAT). However, for such agents, oral activity is crucial. This study focused on further optimizing these compounds to enhance their ligand efficiency, aiming to reduce bulkiness and hydrophobicity, which should improve solubility and, consequently, oral bioavailability. Using Trypanosoma brucei brucei cells as the parasite model and human normal kidney cells and mouse macrophage cells as the host model, we evaluated 30 new analogs synthesized through combinatorial chemistry. These analogs have fewer aromatic moieties and lower molecular weights than their predecessors. Several new analogs demonstrated IC50s in the low micromolar range, effectively inhibiting trypanosome cell growth without harming mammalian cells at the same concentration. We conducted a detailed structure-activity relationship (SAR) analysis and a docking study to assess the compounds\' binding affinity to trypanosome tubulin homolog. The results revealed a correlation between binding energy and anti-Trypanosoma activity. Importantly, compound 7 displayed significant oral activity, effectively inhibiting trypanosome cell proliferation in mice.

The main challenge in the development of agrochemicals is the lack of new leads and/or targets. It is critical to discover new molecular targets and their corresponding ligands. YZK-C22, which contains a 1,2,3-thiadiazol-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazole skeleton, is a fungicide lead compound with broad-spectrum fungicidal activity. Previous studies suggested that the [1,2,4]triazolo[3,4-b][1,3,4]thiadiazole scaffold exhibited good antifungal activity. Inspired by this, a series of pyrrolo[2,3-d]thiazole derivatives were designed and synthesized through a bioisosteric strategy. Compounds C1, C9, and C20 were found to be more active against Rhizoctonia solani than the positive control YZK-C22. More than half of the target compounds provided favorable activity against Botrytis cinerea, where the EC50 values of compounds C4, C6, C8, C10, and C20 varied from 1.17 to 1.77 μg/mL. Surface plasmon resonance and molecular docking suggested that in vitro potent compounds C9 and C20 have a new mode of action instead of acting as pyruvate kinase inhibitors. Transcriptome analysis revealed that compound C20 can impact the tryptophan metabolic pathway, cutin, suberin, and wax biosynthesis of B. cinerea. Overall, pyrrolo[2,3-d]thiazole is discovered as a new fungicidal lead structure with a potential new mode of action for further exploration.

Drug-like properties play pivotal roles in drug adsorption, distribution, metabolism, excretion, and toxicity. Therefore, efficiently optimizing these properties is essential for the successful development of novel therapeutics. Understanding the structure-property relationships of clinically approved drugs can provide valuable insights for drug design and optimization strategies. Among the new drugs approved in 2023, which include 31 small-molecule drugs in the US, the structure-property relationships of nine drugs were compiled from the medicinal chemistry literature, in which detailed information on pharmacokinetic and/or physicochemical properties was reported not only for the final drug but also for its key analogs generated during drug development. The structure-property relationships of nine newly approved drugs are summarized, including three kinase inhibitors and three G-protein-coupled receptor antagonists. Several optimization strategies, such as bioisosteric replacement and steric handle installation, have successfully produced clinical candidates with enhanced physicochemical and pharmacokinetic properties. The summarized structure-property relationships demonstrate how appropriate structural modifications can effectively improve overall drug-like properties. The ongoing exploration of structure- property relationships of clinically approved drugs is expected to offer valuable guidance for developing future drugs.

Molecular docking has become an essential part of a structural biologist\'s and medicinal chemist\'s toolkits. Given a chemical compound and the three-dimensional structure of a molecular target-for example, a protein-docking methods fit the compound into the target, predicting the compound\'s bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand\'s mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.