Computer-Aided Drug Design Using the Fragment Molecular Orbital Method: Current Status and Future Applications for SBDD.

Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.

cPEPmatch Webserver: A comprehensive tool and database to aid rational design of cyclic peptides for drug discovery.

Cyclic peptides have emerged as versatile scaffolds in drug discovery due to their stability and specificity. Here, we present the cPEPmatch webserver (accessible at https://t38webservices.nat.tum.de/cpepmatch/), an easy-to-use interface for the rational design of cyclic peptides targeting protein-protein interactions combined with a semi-quantitative evaluation of binding stability. This platform also offers access to a comprehensive database of cyclic peptide crystal structures. We demonstrate the webserver's utility through a series of case studies involving medically relevant protein systems, highlighting its potential to significantly advance drug discovery efforts.

Novel potent molecular glue degraders against broad range of hematological cancer cell lines via multiple neosubstrates degradation.

BACKGROUND: Targeted protein degradation of neosubstrates plays a crucial role in hematological cancer treatment involving immunomodulatory imide drugs (IMiDs) therapy. Nevertheless, the persistence of inevitable drug resistance and hematological toxicities represents a significant obstacle to their clinical effectiveness. METHODS: Phenotypic profiling of a small molecule compounds library in multiple hematological cancer cell lines was conducted to screen for hit degraders. Molecular dynamic-based rational design and cell-based functional assays were conducted to develop more potent degraders. Multiple myeloma (MM) tumor xenograft models were employed to investigate the antitumor efficacy of the degraders as single or combined agents with standard of care agents. Unbiased proteomics was employed to identify multiple therapeutically relevant neosubstrates targeted by the degraders. MM patient-derived cell lines (PDCs) and a panel of solid cancer cell lines were utilized to investigate the effects of candidate degrader on different stage of MM cells and solid malignancies. Unbiased proteomics of IMiDs-resistant MM cells, cell-based functional assays and RT-PCR analysis of clinical MM specimens were utilized to explore the role of BRD9 associated with IMiDs resistance and MM progression. RESULTS: We identified a novel cereblon (CRBN)-dependent lead degrader with phthalazinone scaffold, MGD-4, which induced the degradation of Ikaros proteins. We further developed a novel potent candidate, MGD-28, significantly inhibited the growth of hematological cancer cells and induced the degradation of IKZF1/2/3 and CK1α with nanomolar potency via a Cullin-CRBN dependent pathway. Oral administration of MGD-4 and MGD-28 effectively inhibited MM tumor growth and exhibited significant synergistic effects with standard of care agents. MGD-28 exhibited preferentially profound cytotoxicity towards MM PDCs at different disease stages and broad antiproliferative activity in multiple solid malignancies. BRD9 modulated IMiDs resistance, and the expression of BRD9 was significant positively correlated with IKZF1/2/3 and CK1α in MM specimens at different stages. We also observed pronounced synergetic efficacy between the BRD9 inhibitor and MGD-28 for MM treatment. CONCLUSIONS: Our findings present a strategy for the multi-targeted degradation of Ikaros proteins and CK1α against hematological cancers, which may be expanded to additional targets and indications. This strategy may enhance efficacy treatment against multiple hematological cancers and solid tumors.

Finding the most potent compounds using active learning on molecular pairs.

Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited and model exploitation might lead to analog identification with limited scaffold diversity. Here, we present ActiveDelta, an adaptive approach that leverages paired molecular representations to predict improvements from the current best training compound to prioritize further data acquisition. We apply the ActiveDelta concept to both graph-based deep (Chemprop) and tree-based (XGBoost) models during exploitative active learning for 99 Ki benchmarking datasets. We show that both ActiveDelta implementations excel at identifying more potent inhibitors compared to the standard exploitative active learning implementations of Chemprop, XGBoost, and Random Forest. The ActiveDelta approach is also able to identify more chemically diverse inhibitors in terms of their Murcko scaffolds. Finally, deep models such as Chemprop trained on data selected through ActiveDelta approaches can more accurately identify inhibitors in test data created through simulated time-splits. Overall, this study highlights the large potential for molecular pairing approaches to further improve popular active learning strategies in low data regimes by enabling faster and more accurate identification of more diverse molecular hits against critical drug targets.

Targeting RNA with small molecules, from RNA structures to precision medicines: IUPHAR review: 40.

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three-dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA-targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA-targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA-binding small molecules. We focus on recent advances in developing RNA-targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early-stage discovery of RNA-binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future.

QSAR Studies on a Class of Benzofuranene Cyanide Derivatives as Potential Inhibitors Targeting Staphylococcus aureus Sortase A.

BACKGROUND: Staphylococcus aureus is a widely distributed and highly pathogenic zoonotic bacterium. Sortase A represents a crucial target for the research and development of novel antibacterial drugs. OBJECTIVE: This study aims to establish quantitative structure-activity relationship models based on the chemical structures of a class of benzofuranene cyanide derivatives. The models will be used to screen new antibacterial agents and predict the properties of these molecules. METHOD: The compounds were randomly divided into a training set and a test set. A large number of descriptors were calculated using the software, and then the appropriate descriptors were selected to build the models through the heuristic method and the gene expression programming algorithm. RESULTS: In the heuristic method, the determination coefficient, determination coefficient of cross-validation, F-test, and mean squared error values were 0.530, 0.395, 9.006, and 0.047, respectively. In the gene expression programming algorithm, the determination coefficient and the mean squared error values in the training set were 0.937 and 0.008, respectively, while in the test set, they were 0.849 and 0.035. The results showed that the minimum bond order of a C atom and the relative number of benzene rings had a significant positive contribution to the activity of compounds. CONCLUSION: In this study, two quantitative structure-activity relationship models were successfully established to predict the inhibitory activity of a series of compounds targeting Staphylococcus aureus Sortase A, providing insights for further development of novel anti-Staphylococcus aureus drugs.

@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking.

Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

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.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

Teaching old docks new tricks with machine learning enhanced ensemble docking.

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound's spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

Ayurinformatics Laboratory- A synergy platform for Ayurveda and technology.

The Ayush sector has attained buoyant growth in the past decade as a science, public health, medicine, and industry. Artificial Intelligence, computational drug designing, and other combinatorial techniques could further accelerate the sector's growth. In this edition, we delve into the confluence of Ayurveda and technology, a theme that resonates profoundly in the contemporary healthcare and wellness landscape. The fusion of Ayurveda, an ancient system of medicine rooted in holistic well-being, with cutting-edge technology, is not just a paradigm shift but a necessary evolution in pursuing an integrated healthcare system where all systems have their defined, recognized, and respected contribution. Here, We are highlight one-such fusion initiative "Ayurinformatics Laboratory".

Sulfonamide inhibitors of bacterial carbonic anhydrases.

The increasing prevalence of antibiotic-resistant bacteria necessitates the exploration of novel therapeutic targets. Bacterial carbonic anhydrases (CAs) have been known for decades, but only in the past ten years they have garnered significant interest as drug targets to develop antibiotics having a diverse mechanism of action compared to the clinically used drugs. Significant progress has been made in the field in the past three years, with the validation in vivo of CAs from Neisseria gonorrhoeae, and vancomycin-resistant enterococci as antibiotic targets. This chapter compiles the state-of-the-art research on sulfonamide derivatives described as inhibitors of all known bacterial CAs. A section delves into the mechanisms of action of sulfonamide compounds with the CA classes identified in pathogenic bacteria, specifically α, ß, and γ classes. Therefore, the inhibitory profiling of the bacterial CAs with classical and clinically used sulfonamide compounds is reported and analyzed. Another section covers various other series of sulfonamide CA inhibitors studied for the development of new antibiotics. By synthesizing current research findings, this chapter highlights the potential of sulfonamide inhibitors as a novel class of antibacterial agents and paves the way for future drug design strategies.

Discovery of the potent covalent inhibitor with an acrylate warhead for SARS-CoV-2 3CL protease.

COVID-19 has caused severe consequences in terms of public health and economy worldwide since its outbreak in December 2019. SARS-CoV-2 3C-like protease (3CLpro), crucial for the viral replications, is an attractive target for the development of antiviral drugs. In this study, several kinds of Michael acceptor warheads were utilized to hunt for potent covalent inhibitors against 3CLpro. Meanwhile, novel 3CLpro inhibitors with the P3-3,5-dichloro-4-(2-(dimethylamino)ethoxy)phenyl moiety were designed and synthesized which may form salt bridge with residue Glu166. Among them, two compounds 12b and 12c exhibited high inhibitory activities against SARS-CoV-2 3CLpro. Further investigations suggested that 12b with an acrylate warhead displayed potent activity against HCoV-OC43 (EC50 = 97 nM) and SARS-CoV-2 replicon (EC50 = 45 nM) and low cytotoxicity (CC50 > 10 µM) in Huh7 cells. Taken together, this study devised two series of 3CLpro inhibitors and provided the potent SARS-CoV-2 3CLpro inhibitor (12b) which may be used for treating coronavirus infections.

Advances in Developing Small Molecule Drugs for Alzheimer's Disease.

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; and the development trend of new drug development for AD in the future.

Aminopyridines in the development of drug candidates against protozoan neglected tropical diseases.

Neglected tropical diseases (NTDs) pose a major threat in tropical zones for impoverished populations. Difficulty of access, adverse effects or low efficacy limit the use of current therapeutic options. Therefore, development of new drugs against NTDs is a necessity. Compounds containing an aminopyridine (AP) moiety are of great interest for the design of new anti-NTD drugs due to their intrinsic properties compared with their closest chemical structures. Currently, over 40 compounds with an AP moiety are on the market, but none is used against NTDs despite active research on APs. The aim of this review is to present the medicinal chemistry work carried out with these scaffolds, against protozoan NTDs: Trypanosoma cruzi, Trypanosoma brucei or Leishmania spp.

[Box: see text].

DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.

PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence-generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.

Prediction of Protein-Drug Interactions, Pharmacophore Modeling, and Toxicokinetics of Novel Leads for Type 2 Diabetes Treatment.

BACKGROUND: Small heterocyclic compounds have been crucial in pioneering advances in type 2 diabetes treatment. There has been a dramatic increase in the pharmacological development of novel heterocyclic derivatives aimed at stimulating the activation of Glucokinase (GK). A pharmaceutical intervention for diabetes is increasingly targeting GK as a legitimate target. Diabetes type 2 compromises Glucokinase's function, an enzyme vital for maintaining the balance of blood glucose levels. Medicinal substances strategically positioned to improve type 2 diabetes management are used to stimulate the GK enzyme using heterocyclic derivatives. OBJECTIVE: The research endeavor aimed to craft novel compounds, drawing inspiration from the inherent coumarin nucleus found in nature. The goal was to evoke the activity of the glucokinase enzyme, offering a tailored approach to mitigate the undesired side effects typically associated with conventional therapies employed in the treatment of type 2 diabetes. METHODS: Coumarin, sourced from nature's embrace, unfolds as a potent and naturally derived ally in the quest for innovative antidiabetic interventions. Coumarin was extracted from a variety of botanical origins, including Artemisia keiskeana, Mallotus resinosus, Jatropha integerrima, Ferula tingitana, Zanthoxylum schinifolium, Phebalium clavatum, and Mammea siamensis. This inclusive evaluation was conducted on Muybridge's digital database containing 53,000 hit compounds. The presence of the coumarin nucleus was found in 100 compounds, that were selected from this extensive repository. Utilizing Auto Dock Vina 1.5.6 and ChemBioDraw Ultra, structures generated through this process underwent docking analysis. Furthermore, these compounds were accurately predicted online log P using the Swiss ADME algorithm. A predictive analysis was conducted using PKCSM software on the primary compounds to assess potential toxicity. RESULTS: Using Auto Dock Vina 1.5.6, 100 coumarin derivatives were assessed for docking. Glucokinase (GK) binding was significantly enhanced by most of these compounds. Based on superior binding characteristics compared with Dorzagliatin (standard GKA) and MRK (co-crystallized ligand), the top eight molecules were identified. After further evaluation through ADMET analysis of these eight promising candidates, it was confirmed that they met the Lipinski rule of five and their pharmacokinetic profile was enhanced. The highest binding affinity was demonstrated by APV16 at -10.6 kcal/mol. A comparison between the APV16, Dorzagliatin and MRK in terms of toxicity predictions using PKCSM indicated that the former exhibited less skin sensitization, AMES toxicity, and hepatotoxicity. CONCLUSION: Glucokinase is most potently activated by 100 of the compound leads in the database of 53,000 compounds that contain the coumarin nucleus. APV12, with its high binding affinity, favorable ADMET (adjusted drug metabolic equivalents), minimal toxicity, and favorable pharmacokinetic profile warrants consideration for progress to in vitro testing. Nevertheless, to uncover potential therapeutic implications, particularly in the context of type 2 diabetes, thorough investigations and in-vivo evaluations are necessary for benchmarking before therapeutic use, especially experiments involving the STZ diabetic rat model.