Flexible scaffold-based cheminformatics approach for polypharmacological drug design.

Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a "bending-down" binding pose at 5-HT2AR to act as an antagonist and a "stretching-up" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the "agonist filter" and "conformation shaper," which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.

To Bind or Not to Bind: Unravelling GPCR Polypharmacology.

Interaction of a single drug with multiple targets through "polypharmacology" is increasingly recognized as necessary for treatment of complex diseases, such as schizophrenia. G protein-coupled receptors (GPCRs) are major medicinal targets, and understanding the structural basis of both GPCR drug selectivity and promiscuity could provide novel avenues for drug development.

Recognition of methamphetamine and other amines by trace amine receptor TAAR1.

Trace amine-associated receptor 1 (TAAR1), the founding member of a nine-member family of trace amine receptors, is responsible for recognizing a range of biogenic amines in the brain, including the endogenous ß-phenylethylamine (ß-PEA)1 as well as methamphetamine2, an abused substance that has posed a severe threat to human health and society3. Given its unique physiological role in the brain, TAAR1 is also an emerging target for a range of neurological disorders including schizophrenia, depression and drug addiction2,4,5. Here we report structures of human TAAR1-G-protein complexes bound to methamphetamine and ß-PEA as well as complexes bound to RO5256390, a TAAR1-selective agonist, and SEP-363856, a clinical-stage dual agonist for TAAR1 and serotonin receptor 5-HT1AR (refs. 6,7). Together with systematic mutagenesis and functional studies, the structures reveal the molecular basis of methamphetamine recognition and underlying mechanisms of ligand selectivity and polypharmacology between TAAR1 and other monoamine receptors. We identify a lid-like extracellular loop 2 helix/loop structure and a hydrogen-bonding network in the ligand-binding pockets, which may contribute to the ligand recognition in TAAR1. These findings shed light on the ligand recognition mode and activation mechanism for TAAR1 and should guide the development of next-generation therapeutics for drug addiction and various neurological disorders.

Ligand recognition and G-protein coupling of trace amine receptor TAAR1.

Trace-amine-associated receptors (TAARs), a group of biogenic amine receptors, have essential roles in neurological and metabolic homeostasis1. They recognize diverse endogenous trace amines and subsequently activate a range of G-protein-subtype signalling pathways2,3. Notably, TAAR1 has emerged as a promising therapeutic target for treating psychiatric disorders4,5. However, the molecular mechanisms underlying its ability to recognize different ligands remain largely unclear. Here we present nine cryo-electron microscopy structures, with eight showing human and mouse TAAR1 in a complex with an array of ligands, including the endogenous 3-iodothyronamine, two antipsychotic agents, the psychoactive drug amphetamine and two identified catecholamine agonists, and one showing 5-HT1AR in a complex with an antipsychotic agent. These structures reveal a rigid consensus binding motif in TAAR1 that binds to endogenous trace amine stimuli and two extended binding pockets that accommodate diverse chemotypes. Combined with mutational analysis, functional assays and molecular dynamic simulations, we elucidate the structural basis of drug polypharmacology and identify the species-specific differences between human and mouse TAAR1. Our study provides insights into the mechanism of ligand recognition and G-protein selectivity by TAAR1, which may help in the discovery of ligands or therapeutic strategies for neurological and metabolic disorders.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/.

A novel strategy for designing the magic shotguns for distantly related target pairs.

Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

Polypharmacology-based kinome screen identifies new regulators of KSHV reactivation.

Kaposi's sarcoma-associated herpesvirus (KSHV) causes several human diseases including Kaposi's sarcoma (KS), a leading cause of cancer in Africa and in patients with AIDS. KS tumor cells harbor KSHV predominantly in a latent form, while typically <5% contain lytic replicating virus. Because both latent and lytic stages likely contribute to cancer initiation and progression, continued dissection of host regulators of this biological switch will provide insights into fundamental pathways controlling the KSHV life cycle and related disease pathogenesis. Several cellular protein kinases have been reported to promote or restrict KSHV reactivation, but our knowledge of these signaling mediators and pathways is incomplete. We employed a polypharmacology-based kinome screen to identify specific kinases that regulate KSHV reactivation. Those identified by the screen and validated by knockdown experiments included several kinases that enhance lytic reactivation: ERBB2 (HER2 or neu), ERBB3 (HER3), ERBB4 (HER4), MKNK2 (MNK2), ITK, TEC, and DSTYK (RIPK5). Conversely, ERBB1 (EGFR1 or HER1), MKNK1 (MNK1) and FRK (PTK5) were found to promote the maintenance of latency. Mechanistic characterization of ERBB2 pro-lytic functions revealed a signaling connection between ERBB2 and the activation of CREB1, a transcription factor that drives KSHV lytic gene expression. These studies provided a proof-of-principle application of a polypharmacology-based kinome screen for the study of KSHV reactivation and enabled the discovery of both kinase inhibitors and specific kinases that regulate the KSHV latent-to-lytic replication switch.

A versatile polypharmacology platform promotes cytoprotection and viability of human pluripotent and differentiated cells.

Human pluripotent stem cells (hPSCs) are capable of extensive self-renewal yet remain highly sensitive to environmental perturbations in vitro, posing challenges to their therapeutic use. There is an urgent need to advance strategies that ensure safe and robust long-term growth and functional differentiation of these cells. Here, we deployed high-throughput screening strategies to identify a small-molecule cocktail that improves viability of hPSCs and their differentiated progeny. The combination of chroman 1, emricasan, polyamines, and trans-ISRIB (CEPT) enhanced cell survival of genetically stable hPSCs by simultaneously blocking several stress mechanisms that otherwise compromise cell structure and function. CEPT provided strong improvements for several key applications in stem-cell research, including routine cell passaging, cryopreservation of pluripotent and differentiated cells, embryoid body (EB) and organoid formation, single-cell cloning, and genome editing. Thus, CEPT represents a unique poly-pharmacological strategy for comprehensive cytoprotection, providing a rationale for efficient and safe utilization of hPSCs.

Medicinal Polypharmacology in the Clinic - Translating the Polypharmacolome into Therapeutic Benefit.

Drugs with multiple targets, often annotated as 'unselective', 'promiscuous', 'multitarget', or 'polypharmacological', are widely considered in both academic and industrial research as a high risk due to the likelihood of adverse effects. However, retrospective analyses have shown that particularly approved drugs bear rich polypharmacological profiles. This raises the question whether our perception of the specificity paradigm ('one drug-one target concept') is correct - and if specifically multitarget drugs should be developed instead of being rejected. These questions provoke a paradigm shift - regarding the development of polypharmacological drugs not as a 'waste of investment', but acknowledging the existence of a 'lack of investment'. This perspective provides an insight into modern drug development highlighting latest drug candidates that have not been assessed in a broader polypharmacology-based context elsewhere embedded in a historic framework of classical and modern approved multitarget drugs. The article shall be an inspiration to the scientific community to re-consider current standards, and more, to evolve to a better understanding of polypharmacology from a challenge to an opportunity.

Investigating Polypharmacology through Targeting Known Human Neutrophil Elastase Inhibitors to Proteinase 3.

Using a combination of multisite λ-dynamics (MSλD) together with in vitro IC50 assays, we evaluated the polypharmacological potential of a scaffold currently in clinical trials for inhibition of human neutrophil elastase (HNE), targeting cardiopulmonary disease, for efficacious inhibition of Proteinase 3 (PR3), a related neutrophil serine proteinase. The affinities we observe suggest that the dihydropyrimidinone scaffold can serve as a suitable starting point for the establishment of polypharmacologically targeting both enzymes and enhancing the potential for treatments addressing diseases like chronic obstructive pulmonary disease.

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development.

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

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.

Polypharmacology-Labeled Molecular Networking: An Analytical Technology Workflow for Accelerated Identification of Multiple Bioactive Constituents in Complex Extracts.

Discovery of sustainable and benign-by-design drugs to combat emerging health pandemics calls for new analytical technologies to explore the chemical and pharmacological properties of Nature's unique chemical space. Here, we present a new analytical technology workflow, polypharmacology-labeled molecular networking (PLMN), where merged positive and negative ionization tandem mass spectrometry-based molecular networking is linked with data from polypharmacological high-resolution inhibition profiling for easy and fast identification of individual bioactive constituents in complex extracts. The crude extract of Eremophila rugosa was subjected to PLMN analysis for the identification of antihyperglycemic and antibacterial constituents. Visually easy-interpretable polypharmacology scores and polypharmacology pie charts as well as microfractionation variation scores of each node in the molecular network provided direct information about each constituent's activity in the seven assays included in this proof-of-concept study. A total of 27 new non-canonical nerylneryl diphosphate-derived diterpenoids were identified. Serrulatane ferulate esters were shown to be associated with antihyperglycemic and antibacterial activities, including some showing synergistic activity with oxacillin in clinically relevant (epidemic) methicillin-resistant Staphylococcus aureus strains and some showing saddle-shaped binding to the active site of protein-tyrosine phosphatase 1B. PLMN is scalable in the number and types of assays included and thus holds potential for a paradigm shift toward polypharmacological natural-products-based drug discovery.

PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method.

MOTIVATION: Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. RESULTS: Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY AND IMPLEMENTATION: The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic.

A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as ß-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants-Vanilla VAE, ß-VAE, and MMD-VAE-on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the ß-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future.

Illuminating the Chemical Space of Untargeted Proteins.

According to the Illuminating the Druggable Genome (IDG) initiative, 90% of the proteins encoded by the human genome still lack an identified active ligand, that is, a small molecule with biologically relevant binding potency or functional activity in an in vitro assay. Under this scenario, there is an urgent need for new approaches to chemically address these yet untargeted proteins. It is widely recognized that the best starting point for generating novel small molecules for proteins is to exploit the expected polypharmacology of known active ligands across phylogenetically related proteins following the paradigm that similar proteins are likely to interact with similar ligands. Here, we introduce a computational strategy to identify privileged structures that, when chemically expanded, are highly probable to contain active small molecules for untargeted proteins. The protocol was first tested on a set of 576 currently targeted proteins having at least one protein family sibling the year before their first active ligand was reported. A privileged structure contained in active ligands that were identified in the following years was correctly anticipated for 214 (37%) of those targeted proteins, a lower-bound recall estimate when considering data completeness issues. When applied to a set of 1184 untargeted potential druggable genes in cancer, the identification of privileged structures from known bioactive ligands of protein family siblings allowed for extracting a priority list of diverse commercially available small molecules for 960 of them. Assuming a minimum success rate of 37%, the chemical library selections should be able to deliver active ligands for at least 355 currently untargeted proteins associated with cancer.

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes.

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

LigAdvisor: a versatile and user-friendly web-platform for drug design.

Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Based on these premises, we developed LigAdvisor, a data-driven webserver which integrates information reported in DrugBank, Protein Data Bank, UniProt, Clinical Trials and Therapeutic Target Database into an intuitive platform, to facilitate drug discovery tasks as drug repurposing, polypharmacology, target fishing and profiling. As designed, LigAdvisor enables easy integration of similarity estimation results with clinical data, thereby allowing a more efficient exploitation of information in different drug discovery contexts. Users can also develop customizable drug design tasks on their own molecules, by means of ligand- and target-based search modes, and download their results. LigAdvisor is publicly available at https://ligadvisor.unimore.it/.

Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets.

In drug discovery, compounds with well-defined activity against multiple targets (multitarget compounds, MT-CPDs) provide the basis for polypharmacology and are thus of high interest. Typically, MT-CPDs for polypharmacology have been discovered serendipitously. Therefore, over the past decade, computational approaches have also been adapted for the design of MT-CPDs or their identification via computational screening. Such approaches continue to be under development and are far from being routine. Recently, different machine learning (ML) models have been derived to distinguish between MT-CPDs and corresponding compounds with activity against the individual targets (single-target compounds, ST-CPDs). When evaluating alternative models for predicting MT-CPDs, we discovered that MT-CPDs could also be accurately predicted with models derived for corresponding ST-CPDs; this was an unexpected finding that we further investigated using explainable ML. The analysis revealed that accurate predictions of ST-CPDs were determined by subsets of structural features of MT-CPDs required for their prediction. These findings provided a chemically intuitive rationale for the successful prediction of MT-CPDs using different ML models and uncovered general-feature subset relationships between MT- and ST-CPDs with activities against different targets.