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.

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.

Omega-3 polyunsaturated fatty acid derived lipid mediators: a comprehensive update on their application in anti-cancer drug discovery.

INTRODUCTION: ω-3 Polyunsaturated fatty acids (PUFAs) have a range of health benefits, including anticancer activity, and are converted to lipid mediators that could be adapted into pharmacological strategies. However, the stability of these mediators must be improved, and they may require formulation to achieve optimal tissue concentrations. AREAS COVERED: Herein, the author reviews the literature around chemical stabilization and formulation of ω-3 PUFA mediators and their application in anticancer drug discovery. EXPERT OPINION: Aryl-urea bioisosteres of ω-3 PUFA epoxides that killed cancer cells targeted the mitochondrion by a novel dual mechanism: as protonophoric uncouplers and as inhibitors of electron transport complex III that activated ER-stress and disrupted mitochondrial integrity. In contrast, aryl-ureas that contain electron-donating substituents prevented cancer cell migration. Thus, aryl-ureas represent a novel class of agents with tunable anticancer properties. Stabilized analogues of other ω-3 PUFA-derived mediators could also be adapted into anticancer strategies. Indeed, a cocktail of agents that simultaneously promote cell killing, inhibit metastasis and angiogenesis, and that attenuate the pro-inflammatory microenvironment is a novel future anticancer strategy. Such regimen may enhance anticancer drug efficacy, minimize the development of anticancer drug resistance and enhance outcomes.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Kinome-Wide Virtual Screening by Multi-Task Deep Learning.

Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome-a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data.

Recent developments on the synthesis of biologically active glycohybrids.

The exploration of hybridization emerges as a potent tool in advancing drug discovery research, with a significant emphasis on carbohydrate-containing hybrid scaffolds. Evidence indicates that linking carbohydrate molecules to privileged bioactive scaffolds enhances the bioactivity of drug molecules. This synergy results in a diverse range of activities, making carbohydrate scaffolds pivotal for synthesizing compound libraries with significant functional and structural diversity. Beyond their synthesis utility, these scaffolds offer applications in screening bioactive molecules, presenting alternative avenues for drug development. This comprehensive review spanning 2015 to 2023 focuses on synthesized glycohybrid molecules, revealing their bioactivity in areas such as anti-microbial, anti-cancer, anti-diabetic, anti-inflammatory activities, enzyme inhibition and pesticides. Numerous novel glycohybrids surpass positive control drugs in biological activity. This focused study not only highlights the diverse bioactivities of glycohybrids but also underscores their promising role in innovative drug development strategies.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

AI for targeted polypharmacology: The next frontier in drug discovery.

In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.

Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce transcriptomics-to-activity transformer (TAT) models that leverage gene expression profiles observed over compound treatment at multiple concentrations to predict the compound activity in other biochemical or cellular assays. We built TAT models based on gene expression data from a RASL-seq assay to predict the activity of 2692 compounds in 262 dose-response assays. We obtained useful models for 51% of the assays, as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several submicromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.

Discovery of 14-3-3 PPI Stabilizers by Extension of an Amidine-Substituted Thiophene Fragment.

Protein-protein interaction (PPI) modulation is a promising approach in drug discovery with the potential to expand the 'druggable' proteome and develop new therapeutic strategies. While there have been significant advancements in methodologies for developing PPI inhibitors, there is a relative scarcity of literature describing the 'bottom-up' development of PPI stabilizers (Molecular Glues). The hub protein 14-3-3 and its interactome provide an excellent platform for exploring conceptual approaches to PPI modulation, including evolution of chemical matter for Molecular Glues. In this study, we employed a fragment extension strategy to discover stabilizers for the complex of 14-3-3 protein and an Estrogen Receptor alpha-derived peptide (ERα). A focused library of analogues derived from an amidine-substituted thiophene fragment enhanced the affinity of the 14-3-3/ERα complex up to 6.2-fold. Structure-activity relationship (SAR) analysis underscored the importance of the newly added, aromatic side chain with a certain degree of rigidity. X-ray structural analysis revealed a unique intermolecular π-π stacking binding mode of the most active analogues, resulting in the simultaneous binding of two molecules to the PPI binding pocket. Notably, analogue 11 displayed selective stabilization of the 14-3-3/ERα complex.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets.

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Computational drug discovery on human immunodeficiency virus with a customized long short-term memory variational autoencoder deep-learning architecture.

Despite attempts to control the spread of human immunodeficiency virus (HIV) through the use of anti-HIV medications, the absence of an effective vaccine continues to present a significant obstacle. In addition, the development of drug resistance by HIV underscores the necessity for computational drug discovery methods to identify novel therapies. This investigation specifically focused on employing a long short-term memory (LSTM) variational autoencoder deep-learning architecture for computational drug discovery in relation to HIV. Our data set comprised simplified molecular input line entry system (SMILES)-encoded compounds, which were used to train the LSTM autoencoder. Remarkably, our model achieved a training accuracy of 91%, with a data set containing 1377 compounds. Leveraging the generative model derived from the training phase, we generated potential new drugs for combating HIV and assessed their interaction with the virus using a previously developed artificial intelligence model. Lastly, we verified the drug likeliness of our computationally generated compounds in accordance with Lipinski's rule of five. Overall, our study presents a promising approach to computational drug discovery in the ongoing battle against HIV.

A simple assay for inhibitors of mycobacterial oxidative phosphorylation.

Oxidative phosphorylation, the combined activities of the electron transport chain (ETC) and ATP synthase, has emerged as a valuable target for antibiotics to treat infection with Mycobacterium tuberculosis and related pathogens. In oxidative phosphorylation, the ETC establishes a transmembrane electrochemical proton gradient that powers ATP synthesis. Monitoring oxidative phosphorylation with luciferase-based detection of ATP synthesis or measurement of oxygen consumption can be technically challenging and expensive. These limitations reduce the utility of these methods for characterization of mycobacterial oxidative phosphorylation inhibitors. Here, we show that fluorescence-based measurement of acidification of inverted membrane vesicles (IMVs) can detect and distinguish between inhibition of the ETC, inhibition of ATP synthase, and nonspecific membrane uncoupling. In this assay, IMVs from Mycobacterium smegmatis are acidified either through the activity of the ETC or ATP synthase, the latter modified genetically to allow it to serve as an ATP-driven proton pump. Acidification is monitored by fluorescence from 9-amino-6-chloro-2-methoxyacridine, which accumulates and quenches in acidified IMVs. Nonspecific membrane uncouplers prevent both succinate- and ATP-driven IMV acidification. In contrast, the ETC Complex III2IV2 inhibitor telacebec (Q203) prevents succinate-driven acidification but not ATP-driven acidification, and the ATP synthase inhibitor bedaquiline prevents ATP-driven acidification but not succinate-driven acidification. We use the assay to show that, as proposed previously, lansoprazole sulfide is an inhibitor of Complex III2IV2, whereas thioridazine uncouples the mycobacterial membrane nonspecifically. Overall, the assay is simple, low cost, and scalable, which will make it useful for identifying and characterizing new mycobacterial oxidative phosphorylation inhibitors.

High-content screening of drug combinations of an mPGES-1 inhibitor in multicellular tumor spheroids leads to mechanistic insights into neuroblastoma chemoresistance.

High-throughput drug screening enables the discovery of new anticancer drugs. Although monolayer cell cultures are commonly used for screening, their limited complexity and translational efficiency require alternative models. Three-dimensional cell cultures, such as multicellular tumor spheroids (MCTS), mimic tumor architecture and offer promising opportunities for drug discovery. In this study, we developed a neuroblastoma MCTS model for high-content drug screening. We also aimed to decipher the mechanisms underlying synergistic drug combinations in this disease model. Several agents from different therapeutic categories and with different mechanisms of action were tested alone or in combination with selective inhibition of prostaglandin E2 by pharmacological inhibition of microsomal prostaglandin E synthase-1 (mPGES-1). After a systematic investigation of the sensitivity of individual agents and the effects of pairwise combinations, GFP-transfected MCTS were used in a confirmatory screen to validate the hits. Finally, inhibitory effects on multidrug resistance proteins were examined. In summary, we demonstrate how MCTS-based high-throughput drug screening has the potential to uncover effective drug combinations and provide insights into the mechanism of synergy between an mPGES-1 inhibitor and chemotherapeutic agents.

DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes.

MOTIVATION: The LINCS L1000 project has collected gene expression profiles for thousands of compounds across a wide array of concentrations, cell lines, and time points. However, conventional analysis methods often fall short in capturing the rich information encapsulated within the L1000 transcriptional dose-response data. RESULTS: We present DOSE-L1000, a database that unravels the potency and efficacy of compound-gene pairs and the intricate landscape of compound-induced transcriptional changes. Our study uses the fitting of over 140 million generalized additive models and robust linear models, spanning the complete spectrum of compounds and landmark genes within the LINCS L1000 database. This systematic approach provides quantitative insights into differential gene expression and the potency and efficacy of compound-gene pairs across diverse cellular contexts. Through examples, we showcase the application of DOSE-L1000 in tasks such as cell line and compound comparisons, along with clustering analyses and predictions of drug-target interactions. DOSE-L1000 fosters applications in drug discovery, accelerating the transition to omics-driven drug development. AVAILABILITY AND IMPLEMENTATION: DOSE-L1000 is publicly available at https://doi.org/10.5281/zenodo.8286375.

Artificial intelligence: Machine learning approach for screening large database and drug discovery.

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

Crystallographic fragment screening in academic cancer drug discovery.

Fragment-based drug discovery (FBDD) has brought several drugs to the clinic, notably to target proteins once considered to be challenging, or even undruggable. Screening in FBDD relies upon observing and/or measuring weak (millimolar-scale) binding events using biophysical techniques or crystallographic fragment screening. This latter structural approach provides no information about binding affinity but can reveal binding mode and atomic detail on protein-fragment interactions to accelerate hit-to-lead development. In recent years, high-throughput platforms have been developed at synchrotron facilities to screen thousands of fragment-soaked crystals. However, using accessible manual techniques it is possible to run informative, smaller-scale screens within an academic lab setting. This chapter describes general protocols for home laboratory-scale fragment screening, from fragment soaking through to structure solution and, where appropriate, signposts to background, protocols or alternatives elsewhere.

ABPP-CoDEL: Activity-Based Proteome Profiling-Guided Discovery of Tyrosine-Targeting Covalent Inhibitors from DNA-Encoded Libraries.

DNA-encoded chemical library (DEL) has been extensively used for lead compound discovery for decades in academia and industry. Incorporating an electrophile warhead into DNA-encoded compounds recently permitted the discovery of covalent ligands that selectively react with a particular cysteine residue. However, noncysteine residues remain underexplored as modification sites of covalent DELs. Herein, we report the design and utility of tyrosine-targeting DELs of 67 million compounds. Proteome-wide reactivity analysis of tyrosine-reactive sulfonyl fluoride (SF) covalent probes suggested three enzymes (phosphoglycerate mutase 1, glutathione s-transferase 1, and dipeptidyl peptidase 3) as models of tyrosine-targetable proteins. Enrichment with SF-functionalized DELs led to the identification of a series of tyrosine-targeting covalent inhibitors of the model enzymes. In-depth mechanistic investigation revealed their novel modes of action and reactive ligand-accessible hotspots of the enzymes. Our strategy of combining activity-based proteome profiling and covalent DEL enrichment (ABPP-CoDEL), which generated selective covalent binders against a variety of target proteins, illustrates the potential use of this methodology in further covalent drug discovery.

Drug discovery for heart failure targeting myosin-binding protein C.

Cardiac MyBP-C (cMyBP-C) interacts with actin and myosin to fine-tune cardiac muscle contractility. Phosphorylation of cMyBP-C, which reduces the binding of cMyBP-C to actin and myosin, is often decreased in patients with heart failure (HF) and is cardioprotective in model systems of HF. Therefore, cMyBP-C is a potential target for HF drugs that mimic its phosphorylation and/or perturb its interactions with actin or myosin. We labeled actin with fluorescein-5-maleimide (FMAL) and the C0-C2 fragment of cMyBP-C (cC0-C2) with tetramethylrhodamine (TMR). We performed two complementary high-throughput screens (HTS) on an FDA-approved drug library, to discover small molecules that specifically bind to cMyBP-C and affect its interactions with actin or myosin, using fluorescence lifetime (FLT) detection. We first excited FMAL and detected its FLT, to measure changes in fluorescence resonance energy transfer (FRET) from FMAL (donor) to TMR (acceptor), indicating binding. Using the same samples, we then excited TMR directly, using a longer wavelength laser, to detect the effects of compounds on the environmentally sensitive FLT of TMR, to identify compounds that bind directly to cC0-C2. Secondary assays, performed on selected modulators with the most promising effects in the primary HTS assays, characterized the specificity of these compounds for phosphorylated versus unphosphorylated cC0-C2 and for cC0-C2 versus C1-C2 of fast skeletal muscle (fC1-C2). A subset of identified compounds modulated ATPase activity in cardiac and/or skeletal myofibrils. These assays establish the feasibility of the discovery of small-molecule modulators of the cMyBP-C-actin/myosin interaction, with the ultimate goal of developing therapies for HF.