From molecules to visuals: Empowering drug discovery and development with mass spectrometry imaging.

Over the past three decades, mass spectrometry imaging (MSI) has emerged as a valuable tool for the spatial localization of drugs and metabolites directly from tissue surfaces without the need for labels. MSI offers molecular specificity, making it increasingly popular in the pharmaceutical industry compared to conventional imaging techniques like quantitative whole-body autoradiography (QWBA) and immunohistochemistry, which are unable to distinguish parent drugs from metabolites. Across the industry, there has been a consistent uptake in the utilization of MSI to investigate drug and metabolite distribution patterns, and the integration of MSI with omics technologies in preclinical investigations. To continue the further adoption of MSI in drug discovery and development, we believe there are two key areas that need to be addressed. First, there is a need for accurate quantification of analytes from MSI distribution studies. Second, there is a need for increased interactions with regulatory agencies for guidance on the utility and incorporation of MSI techniques in regulatory filings. Ongoing efforts are being made to address these areas, and it is hoped that MSI will gain broader utilization within the industry, thereby becoming a critical ingredient in driving drug discovery and development.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Discovery and Structure-Activity Relationship of a Ryanodine Receptor 2 Inhibitor.

Ryanodine receptor 2 (RyR2) is a large Ca2+-release channel in the sarcoplasmic reticulum (SR) of cardiac muscle cells. It serves to release Ca2+ from the SR into the cytosol to initiate muscle contraction. RyR2 overactivation is associated with arrhythmogenic cardiac disease, but few specific inhibitors have been reported so far. Here, we identified an RyR2-selective inhibitor 1 from the chemical compound library and synthesized it from glycolic acid. Synthesis of various derivatives to investigate the structure-activity relationship of each substructure afforded another two RyR2-selective inhibitors 6 and 7, among which 6 was the most potent. Notably, compound 6 also inhibited Ca2+ release in cells expressing the RyR2 mutants R2474S, R4497C and K4750Q, which are associated with cardiac arrhythmias such as catecholaminergic polymorphic ventricular tachycardia (CPVT). This inhibitor is expected to be a useful tool for research on the structure and dynamics of RyR2, as well as a lead compound for the development of drug candidates to treat RyR2-related cardiac disease.

Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.

Activity cliffs (ACs) are pairs of structurally similar molecules with significantly different affinities for a biotarget, posing a challenge in computer-assisted drug discovery. This study focuses on protein kinases, significant therapeutic targets, with some exhibiting ACs while others do not despite numerous inhibitors. The hypothesis that the presence of ACs is dependent on the target protein and its complete structural context is explored. Machine learning models were developed to link protein properties to ACs, revealing specific tripeptide sequences and overall protein properties as critical factors in ACs occurrence. The study highlights the importance of considering the entire protein matrix rather than just the binding site in understanding ACs. This research provides valuable insights for drug discovery and design, paving the way for addressing ACs-related challenges in modern computational approaches.

Discovery of a novel and highly selective JAK3 inhibitor as a potent hair growth promoter.

JAK-STAT signalling pathway inhibitors have emerged as promising therapeutic agents for the treatment of hair loss. Among different JAK isoforms, JAK3 has become an ideal target for drug discovery because it only regulates a narrow spectrum of γc cytokines. Here, we report the discovery of MJ04, a novel and highly selective 3-pyrimidinylazaindole based JAK3 inhibitor, as a potential hair growth promoter with an IC50 of 2.03 nM. During in vivo efficacy assays, topical application of MJ04 on DHT-challenged AGA and athymic nude mice resulted in early onset of hair regrowth. Furthermore, MJ04 significantly promoted the growth of human hair follicles under ex-vivo conditions. MJ04 exhibited a reasonably good pharmacokinetic profile and demonstrated a favourable safety profile under in vivo and in vitro conditions. Taken together, we report MJ04 as a highly potent and selective JAK3 inhibitor that exhibits overall properties suitable for topical drug development and advancement to human clinical trials.

A probabilistic knowledge graph for target identification.

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

Discovery of novel FGFR4 inhibitors through a build-up fragment strategy.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death. FGFR4 has been implicated in HCC progression, making it a promising therapeutic target. We introduce an approach for identifying novel FGFR4 inhibitors by sequentially adding fragments to a common warhead unit. This strategy resulted in the discovery of a potent inhibitor, 4c, with an IC50 of 33 nM and high selectivity among members of the FGFR family. Although further optimisation is required, our approach demonstrated the potential for discovering potent FGFR4 inhibitors for HCC treatment, and provides a useful method for obtaining hit compounds from small fragments.

Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.

Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.

A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery.

This dataset represents a collection of pocket-centric structural data related to protein-protein interactions (PPIs) and PPI-related ligand binding sites. The dataset includes high-quality structural information on more than 23,000 pockets, 3,700 proteins on more than 500 organisms, and nearly 3500 ligands that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes with more than 1,700 unique protein families including some with associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for hypothesizing protein partners repurposing.

MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery.

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.

Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles.

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.

AI is a viable alternative to high throughput screening: a 318-target study.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Bioisoteres for carboxylic acids: From ionized isosteres to novel unionized replacements.

Carboxylic acids are key pharmacophoric elements in many molecules. They can be seen as a problem by some, due to perceived permeability challenges, potential for high plasma protein binding and the risk of forming reactive metabolites due to acyl-glucuronidation. By others they are viewed more favorably as they can decrease lipophilicity by adding an ionizable center which can be beneficial for solubility, and can add enthalpic interactions with the target protein. However, there are many instances where the replacement of a carboxylic acid with a bioisosteric group is required. This has led to the development of a number of ionizable groups which sufficiently mimic the carboxylic acid functionality whilst improving, for example, the metabolic profile of the molecule in question. An alternative strategy involves replacement of the carboxylate by neutral functional groups. This review initially details carefully selected examples whereby tetrazoles, acyl sulfonamides or isoxazolols have been beneficially utilized as carboxylic acid bioisosteres altering physicohemical properties, interactions with the target and metabolism and/or pharmacokinetics, before delving further into the binding mode of carboxylic acid derivatives with their target proteins. This analysis highlights new ways to consider the replacement of carboxylic acids by neutral bioisosteric groups which either rely on hydrogen bonds or cation-π interactions. It should serve as a useful guide for scientists working in drug discovery.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene.

In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints. The deep learning model was designed to learn complex patterns and relationships between the pharmacophore features and the corresponding activity/inactivity labels of the molecules. By utilizing this integrated approach, we aimed to enhance the accuracy and efficiency of activity prediction. To validate the effectiveness of our approach, we conducted experiments using a dataset of known molecules with BRCA1 gene activity/inactivity from diverse sources. Our results demonstrated promising predictive performance, indicating the successful integration of pharmacophore modeling and deep learning. Furthermore, we utilized the trained model to predict the activity/inactivity of unknown molecules extracted from the ChEMBL database. The predictions obtained from the ChEMBL database were assessed and compared against experimentally determined values to evaluate the reliability and generalizability of our model. Overall, our proposed approach showcased significant potential in accurately predicting the activity/inactivity of molecules with the BRCA1 gene, thus enabling the identification of potential candidates for further investigation in drug discovery and development processes.

Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling.

The accurate identification of drug-target affinity (DTA) is crucial for advancements in drug discovery and development. Many deep learning-based approaches have been devised to predict drug-target binding affinity accurately, exhibiting notable improvements in performance. However, the existing prediction methods often fall short of capturing the global features of proteins. In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming other baseline methods. The evaluation results on the KIBA dataset reveal that our model achieves the lowest mean square error (MSE) of 0.125, representing a 0.6% reduction compared to the lowest-performing baseline method. Furthermore, the incorporation of queries, keys and values produced by the stacked convolutional neural network (CNN) enables our model to better integrate the local and global context of protein representation, leading to further improvements in the accuracy of DTA prediction.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

1D and 2D NMR for KRAS:Ligand Binding.

Fragment-based screening by ligand-observed 1D NMR and binding interface mapping by protein-observed 2D NMR are popular methods used in drug discovery. These methods allow researchers to detect compound binding over a wide range of affinities and offer a simultaneous assessment of solubility, purity, and chemical formula accuracy of the target compounds and the 15N-labeled protein when examined by 1D and 2D NMR, respectively. These methods can be applied for screening fragment binding to the active (GMPPNP-bound) and inactive (GDP-bound) states of oncogenic KRAS mutants.

KRAS4b:RAF-1 Homogenous Time-Resolved Fluorescence Resonance Energy Transfer Assay for Drug Discovery.

Homogenous time-resolved FRET (HTRF) assays have become one of the most popular tools for pharmaceutical drug screening efforts over the last two decades. Large Stokes shifts and long fluorescent lifetimes of lanthanide chelates lead to robust signal to noise, as well as decreased false positive rates compared to traditional assay techniques. In this chapter, we describe an HTRF protein-protein interaction (PPI) assay for the KRAS4b G-domain in the GppNHp-bound state and the RAF-1-RBD currently used for drug screens. Application of this assay contributes to the identification of lead compounds targeting the GTP-bound active state of K-RAS.