Project description:Target-directed dynamic combinatorial chemistry has emerged as a useful tool for hit identification, but has not been widely used, in part due to challenges associated with analyses involving complex mixtures. We describe an operationally simple alternative: in situ inhibitor synthesis and screening (ISISS), which links high-throughput bioorthogonal synthesis with screening for target binding by fluorescence. We exemplify the ISISS method by showing how coupling screening for target binding by fluorescence polarization with the reaction of acyl-hydrazides and aldehydes led to the efficient discovery of a potent and novel acylhydrazone-based inhibitor of human prolyl hydroxylase 2 (PHD2), a target for anemia treatment, with equivalent in vivo potency to an approved medicine.
Project description:The sensitivity of fluorescence polarization (FP) and fluorescence anisotropy (FA) to molecular weight changes has enabled the interrogation of diverse biological mechanisms, ranging from molecular interactions to enzymatic activity. Assays based on FP/FA technology have been widely utilized in high-throughput screening (HTS) and drug discovery due to the homogenous format, robust performance and relative insensitivity to some types of interferences, such as inner filter effects. Advancements in assay design, fluorescent probes, and technology have enabled the application of FP assays to increasingly complex biological processes. Herein we discuss different types of FP/FA assays developed for HTS, with examples to emphasize the diversity of applicable targets. Furthermore, trends in target and fluorophore selection, as well as assay type and format, are examined using annotated HTS assays within the PubChem database. Finally, practical considerations for the successful development and implementation of FP/FA assays for HTS are provided based on experience at our center and examples from the literature, including strategies for flagging interference compounds among a list of hits.
Project description:In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce VirtuDockDL, which is a streamlined Python-based web platform utilizing deep learning for drug discovery. This pipeline employs a Graph Neural Network to analyze and predict the effectiveness of various compounds as potential drug candidates. During the validation phase, VirtuDockDL was instrumental in identifying non-covalent inhibitors against the VP35 protein of the Marburg virus, a critical target given the virus's high fatality rate and limited treatment options. Further, in benchmarking, VirtuDockDL achieved 99% accuracy, an F1 score of 0.992, and an AUC of 0.99 on the HER2 dataset, surpassing DeepChem (89% accuracy) and AutoDock Vina (82% accuracy). Compared to RosettaVS, MzDOCK, and PyRMD, VirtuDockDL outperformed them by combining both ligand- and structure-based screening with deep learning. While RosettaVS excels in accurate docking but lacks high-throughput screening, and PyRMD focuses on ligand-based methods without AI integration, VirtuDockDL offers superior predictive accuracy and full automation for large-scale datasets, making it ideal for comprehensive drug discovery workflows. These results underscore the tool's capability to identify high-affinity inhibitors accurately across various targets, including the HER2 protein for cancer therapy, TEM-1 beta-lactamase for bacterial infections, and the CYP51 enzyme for fungal infections like Candidiasis. To sum up, VirtuDockDL combines user-friendly interface design with powerful computational capabilities to facilitate rapid, cost-effective drug discovery and development. The integration of AI in drug discovery could potentially transform the landscape of pharmaceutical research, providing faster responses to global health challenges. The VirtuDockDL is available at https://github.com/FatimaNoor74/VirtuDockDL .
Project description:Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions-unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.
Project description:Deep learning contributes significantly to researches in biological sciences and drug discovery. Previous studies suggested that deep learning techniques have shown superior performance to other machine learning algorithms in virtual screening, which is a critical step to accelerate the drug discovery. However, the application of deep learning techniques in drug discovery and chemical biology are hindered due to the data availability, data further processing and lacking of the user-friendly deep learning tools and interface. Therefore, we developed a user-friendly web server with integration of the state of art deep learning algorithm, which utilizes either the public or user-provided dataset to help biologists or chemists perform virtual screening either the chemical probes or drugs for a specific target of interest. With DeepScreening, user could conveniently construct a deep learning model and generate the target-focused de novo libraries. The constructed classification and regression models could be subsequently used for virtual screening against the generated de novo libraries, or diverse chemical libraries in stock. From deep models training to virtual screening, and target focused de novo library generation, all those tasks could be finished with DeepScreening. We believe this deep learning-based web server will benefit to both biologists and chemists for probes or drugs discovery.
Project description:The Malaria Drug Accelerator (MalDA) is a consortium of 15 leading scientific laboratories. The aim of MalDA is to improve and accelerate the early antimalarial drug discovery process by identifying new, essential, druggable targets. In addition, it seeks to produce early lead inhibitors that may be advanced into drug candidates suitable for preclinical development and subsequent clinical testing in humans. By sharing resources, including expertise, knowledge, materials, and reagents, the consortium strives to eliminate the structural barriers often encountered in the drug discovery process. Here we discuss the mission of the consortium and its scientific achievements, including the identification of new chemically and biologically validated targets, as well as future scientific directions.
Project description:Deubiquitinating enzymes (DUBs) are an emerging drug target class of ~100 proteases that cleave ubiquitin from protein substrates to regulate many cellular processes. A lack of selective chemical probes impedes pharmacologic interrogation of this important gene family. DUBs engage their cognate ligands through a myriad of interactions. We embrace this structural complexity to tailor a chemical diversification strategy for a DUB-focused covalent library. Pairing our library with activity-based protein profiling as a high-density primary screen, we identify selective hits against 23 endogenous DUBs spanning four subfamilies. Optimization of an azetidine hit yields a probe for the understudied DUB VCPIP1 with nanomolar potency and in-family selectivity. Our success in identifying good chemical starting points as well as structure-activity relationships across the gene family from a modest but purpose-build library challenges current paradigms that emphasize ultrahigh throughput in vitro or virtual screens against an ever-increasing scope of chemical space.
Project description:The large unmet demand for new heart failure therapeutics is widely acknowledged. Over the last decades the contractile myofilaments themselves have emerged as an attractive target for the development of new therapeutics for both systolic and diastolic heart failure. However, the clinical use of myofilament-directed drugs has been limited, and further progress has been hampered by incomplete understanding of myofilament function on the molecular level and screening technologies for small molecules that accurately reproduce this function in vitro. In this study we have designed, validated and characterized new high throughput screening platforms for small molecule effectors targeting the interactions between the troponin C and troponin I subunits of the cardiac troponin complex. Fluorescence polarization-based assays were used to screen commercially available compound libraries, and hits were validated using secondary screens and orthogonal assays. Hit compound-troponin interactions were characterized using isothermal titration calorimetry and NMR spectroscopy. We identified NS5806 as novel calcium sensitizer that stabilizes active troponin. In good agreement, NS5806 greatly increased the calcium sensitivity and maximal isometric force of demembranated human donor myocardium. Our results suggest that sarcomeric protein-directed screening platforms are suitable for the development of compounds that modulate cardiac myofilament function.
Project description:Considerable advances have been made in the development of micro-physiological systems that seek to faithfully replicate the complexity and functionality of animal and human physiology in research laboratories. Sometimes referred to as "organs-on-chips", these systems provide key insights into physiological or pathological processes associated with health maintenance and disease control, and serve as powerful platforms for new drug development and toxicity screening. In this Focus article, we review the state-of-the-art designs and examples for developing multiple "organs-on-chips", and discuss the potential of this emerging technology to enhance our understanding of human physiology, and to transform and accelerate the drug discovery and preclinical testing process. This Focus article highlights some of the recent technological advances in this field, along with the challenges that must be addressed for these technologies to fully realize their potential.
Project description:O-GlcNAcylation is an essential posttranslational modification in metazoa. Modulation of O-GlcNAc levels with small molecule inhibitors of O-GlcNAc hydrolase (OGA) is a useful strategy to probe the role of this modification in a range of cellular processes. Here we report the discovery of novel, low molecular weight and drug-like O-GlcNAcase inhibitor scaffolds by high-throughput screening. Kinetic and X-ray crystallographic analyses of the binding modes with human/bacterial O-GlcNAcases identify some of these as competitive inhibitors. Comparative kinetic experiments with the mechanistically related human lysosomal hexosaminidases reveal that three of the inhibitor scaffolds show selectivity towards human OGA. These scaffolds provide attractive starting points for the development of non-carbohydrate, drug-like OGA inhibitors.