Drug Discovery Paradigm Research Articles

Button-Push On-Demand Synthesis for Rapid Optimization of Antiviral Peptidomimetics.

The optimization of hit compounds into drug candidates is a pivotal phase in drug discovery but often hampered by cumbersome manual synthesis of derivatives. While automated organic molecule synthesis has enhanced efficiency, safety, and cost-effectiveness, achieving fully automated multistep synthesis remains a formidable challenge due to issues such as solvent and reagent incompatibilities and the accumulation of side-products. We herein demonstrate an automated solid-phase flow platform for synthesizing α-keto-amides and nitrile peptidomimetics, guided by docking simulations, to identify potent broad-spectrum antiviral leads. A compact parallel synthesizer was built in-house, capable of producing 5 distinct molecules per cycle; 525 reactions could be finished within three months to generate 42 derivatives for a structure-activity relationship (SAR) investigation. Among these, ten derivatives exhibited promising target inhibitory activity (IC50 < 100 nM) including two with antiviral activity (EC50 < 250 nM). The platform, coupled with digital chemical recipe files, offers rapid access to a wide range of peptidomimetics, serving as a valuable reservoir for broad-spectrum antiviral candidates. This automated solid-phase flow synthesis approach expedites the generation of previously difficult complex molecular scaffolds. By integration of SPS-flow synthesis with medicinal chemistry campaign, >10-fold target inhibitory activity was achieved from a small set of derivatives, which indicates the potential to shift the paradigm of drug discovery.

A phenotypic drug discovery approach by latent interaction in deep learning

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis -regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical–genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

GexMolGen: cross-modal generation of hit-like molecules via large language model encoding of gene expression signatures.

Designing de novo molecules with specific biological activity is an essential task since it holds the potential to bypass the exploration of target genes, which is an initial step in the modern drug discovery paradigm. However, traditional methods mainly screen molecules by comparing the desired molecular effects within the documented experimental results. The data set limits this process, and it is hard to conduct direct cross-modal comparisons. Therefore, we propose a solution based on cross-modal generation called GexMolGen (Gene Expression-based Molecule Generator), which generates hit-like molecules using gene expression signatures alone. These signatures are calculated by inputting control and desired gene expression states. Our model GexMolGen adopts a "first-align-then-generate" strategy, aligning the gene expression signatures and molecules within a mapping space, ensuring a smooth cross-modal transition. The transformed molecular embeddings are then decoded into molecular graphs. In addition, we employ an advanced single-cell large language model for input flexibility and pre-train a scaffold-based molecular model to ensure that all generated molecules are 100% valid. Empirical results show that our model can produce molecules highly similar to known references, whether feeding in- or out-of-domain transcriptome data. Furthermore, it can also serve as a reliable tool for cross-modal screening.

Cancer pharmacoinformatics: Databases and analytical tools.

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Meta Learning With Graph Attention Networks for Low-Data Drug Discovery.

Finding candidate molecules with favorable pharmacological activity, low toxicity, and proper pharmacokinetic properties is an important task in drug discovery. Deep neural networks have made impressive progress in accelerating and improving drug discovery. However, these techniques rely on a large amount of label data to form accurate predictions of molecular properties. At each stage of the drug discovery pipeline, usually, only a few biological data of candidate molecules and derivatives are available, indicating that the application of deep neural networks for low-data drug discovery is still a formidable challenge. Here, we propose a meta learning architecture with graph attention network, Meta-GAT, to predict molecular properties in low-data drug discovery. The GAT captures the local effects of atomic groups at the atom level through the triple attentional mechanism and implicitly captures the interactions between different atomic groups at the molecular level. GAT is used to perceive molecular chemical environment and connectivity, thereby effectively reducing sample complexity. Meta-GAT further develops a meta learning strategy based on bilevel optimization, which transfers meta knowledge from other attribute prediction tasks to low-data target tasks. In summary, our work demonstrates how meta learning can reduce the amount of data required to make meaningful predictions of molecules in low-data scenarios. Meta learning is likely to become the new learning paradigm in low-data drug discovery. The source code is publicly available at: https://github.com/lol88/Meta-GAT.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs.

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Paeoniae Radix Alba and Network Pharmacology Approach for Osteoarthritis: A Review

Osteoarthritis (OA) is the most common type of arthritis and affects more than 240 million people worldwide; the most frequently affected areas are the hips, knees, feet, and hands. OA pathophysiology is multifactorial, involving genetic, developmental, metabolic, traumatic, and inflammation factors. Therefore, treatments able to address several path mechanisms can help control OA. Network pharmacology is developing as a next-generation research strategy to shift the paradigm of drug discovery from “one drug, one target” to “multi-component, multi-target”. In this paper, network pharmacology is employed to investigate the potential role of Paeoniae Radix Alba (PRA) in the treatment of OA. PRA is a natural product known for its protective effects against OA, which has recently drawn attention because of its ability to provide physiological benefits with fewer toxic effects. This review highlights the anti-inflammatory properties of PRA in treating OA. PRA can be used alone or in combination with conventional therapies to enhance their effectiveness and reduce side effects. The study also demonstrates the use of network pharmacology as a cost-effective and time-saving method for predicting therapeutic targets of PRA in OA treatment.

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.

Active metabolites combination therapies: towards the next paradigm for more efficient and more scientific Chinese medicine.

Traditional Chinese medicine (TCM) formulae have been studied extensively in various human diseases and have proven to be effective due to their multi-component, multi-target advantage. However, its active metabolites are not clear and the specific mechanisms are not well established, which limits its scientific application. Recently, combination therapies are attracting increasing attention from the scientific community in the past few years and are considered as the next paradigm in drug discovery. Here, we tried to define a new concept of "active metabolites combination therapies (AMCT)" rules to elucidate how the bioactive metabolites from TCMs to produce their synergistic effects in this review. The AMCT rules integrate multidisciplinary technologies like molecular biology, biochemistry, pharmacology, analytical chemistry and pharmacodynamics, etc. Meanwhile, emerging technologies such as multi-omics combined analysis, network analysis, artificial intelligence conduce to better elucidate the mechanisms of these combination therapies in disease treatment, which provides new insights for the development of novel active metabolites combination drugs. AMCT rules will hopefully further guide the development of novel combination drugs that will promote the modernization and international needs of TCM.

Digitalization of toxicology: improving preclinical to clinical translation.

Though the portfolio of medicines that are extending and improving the lives of patients continues to grow, drug discovery and development remains a challenging business on its best day. Safety liabilities are a significant contributor to development attrition where the costliest liabilities to both drug developers and patients emerge in late development or post-marketing. Animal studies are an important and influential contributor to the current drug discovery and development paradigm intending to provide evidence that a novel drug candidate can be used safely and effectively in human volunteers and patients. However, translational gaps-such as toxicity in patients not predicted by animal studies-have prompted efforts to improve their effectiveness, especially in safety assessment. More holistic monitoring and "digitalization" of animal studies has the potential to enrich study outcomes leading to datasets that are more computationally accessible, translationally relevant, replicable, and technically efficient. Continuous monitoring of animal behavior and physiology enables longitudinal assessment of drug effects, detection of effects during the animal's sleep and wake cycles and the opportunity to detect health or welfare events earlier. Automated measures can also mitigate human biases and reduce subjectivity. Reinventing a conservative, standardized, and traditional paradigm like drug safety assessment requires the collaboration and contributions of a broad and multi-disciplinary stakeholder group. In this perspective, we review the current state of the field and discuss opportunities to improve current approaches by more fully leveraging the power of sensor technologies, artificial intelligence (AI), and animal behavior in a home cage environment.

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.

Gut microbiota-based discovery of Houttuyniae Herba as a novel prebiotic of Bacteroides thetaiotaomicron with anti-colitis activity

Ulcerative colitis (UC) represents an inflammatory disease characterized by fluctuations in severity, posing substantial challenges in treatment. The gut microbiota plays a pivotal role in the pathogenesis of UC. This study sought to identify drugs specifically targeting the gut microbiota to mitigate UC. We initiated a meta-analysis on gut microbiota in UC patients to identify UC-associated bacterial strains. Subsequently, we screened 164 dietary herbal medicines in vitro to identify potential prebiotics for the UC-associated bacterium, Bacteroides thetaiotaomicron. The DSS-induced colitis mouse model was utilized to evaluate the anti-colitis efficacy of the identified dietary herbal medicine. Full-length 16 S rRNA amplicon sequencing was employed to observe changes in gut microbiota following dietary herbal medicine intervention. The relative abundance of Bacteroides was notably diminished in UC patients compared to their healthy counterparts. B. thetaiotaomicron exhibited an inverse relationship with UC symptoms, indicating its potential as an anti-colitis agent. In vitro assessments revealed that H. Herba significantly bolstered the proliferation of B. thetaiotaomicron. Further experiments showed that treating DSS-induced mice with an aqueous extract of H. Herba considerably alleviated colitis indicators such as weight loss, colon shortening, disease activity score (DAI), and systemic inflammation. Microbial analysis revealed B. thetaiotaomicron as the sole bacterium substantially augmented by H. Herba in vivo. Overall H. Herba emerges as a promising prebiotic for B. thetaiotaomicron, offering significant anti-colitis benefits. Employing a gut microbiota-centric approach proves valuable in the quest for drug discovery.This study provides a new paradigm for drug discovery that targets the gut microbiota to treat UC.

Deep reinforcement learning enables better bias control in benchmark for virtual screening

Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.

Proteolysis-targeting drug delivery system (ProDDS): integrating targeted protein degradation concepts into formulation design.

Targeted protein degradation (TPD) has emerged as a revolutionary paradigm in drug discovery and development, offering a promising avenue to tackle challenging therapeutic targets. Unlike traditional drug discovery approaches that focus on inhibiting protein function, TPD aims to eliminate proteins of interest (POIs) using modular chimeric structures. This is achieved through the utilization of proteolysis-targeting chimeras (PROTACs), which redirect POIs to E3 ubiquitin ligases, rendering them for degradation by the cellular ubiquitin-proteasome system (UPS). Additionally, other TPD technologies such as lysosome-targeting chimeras (LYTACs) and autophagy-based protein degraders facilitate the transportation of proteins to endo-lysosomal or autophagy-lysosomal pathways for degradation, respectively. Despite significant growth in preclinical TPD research, many chimeras fail to progress beyond this stage in the drug development. Various factors contribute to the limited success of TPD agents, including a significant hurdle of inadequate delivery to the target site. Integrating TPD into delivery platforms could surmount the challenges of in vivo applications of TPD strategies by reshaping their pharmacokinetics and pharmacodynamic profiles. These proteolysis-targeting drug delivery systems (ProDDSs) exhibit superior delivery performance, enhanced targetability, and reduced off-tissue side effects. In this review, we will survey the latest progress in TPD-inspired drug delivery systems, highlight the importance of introducing delivery ideas or technologies to the development of protein degraders, outline design principles of protein degrader-inspired delivery systems, discuss the current challenges, and provide an outlook on future opportunities in this field.

Network-Based Methods and Their Applications in Drug Discovery.

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Mechanisms for cardiac calcium pump activation by its substrate and a synthetic allosteric modulator using fluorescence lifetime imaging.

The discovery of allosteric modulators is an emerging paradigm in drug discovery, and signal transduction is a subtle and dynamic process that is challenging to characterize. We developed a time-correlated single photon-counting imaging approach to investigate the structural mechanisms for small-molecule activation of the cardiac sarcoplasmic reticulum Ca2+-ATPase, a pharmacologically important pump that transports Ca2+ at the expense of adenosine triphosphate (ATP) hydrolysis. We first tested whether the dissociation of sarcoplasmic reticulum Ca2+-ATPase from its regulatory protein phospholamban is required for small-molecule activation. We found that CDN1163, a validated sarcoplasmic reticulum Ca2+-ATPase activator, does not have significant effects on the stability of the sarcoplasmic reticulum Ca2+-ATPase-phospholamban complex. Time-correlated single photon-counting imaging experiments using the nonhydrolyzable ATP analog β,γ-Methyleneadenosine 5'-triphosphate (AMP-PCP) showed ATP is an allosteric modulator of sarcoplasmic reticulum Ca2+-ATPase, increasing the fraction of catalytically competent structures at physiologically relevant Ca2+ concentrations. Unlike ATP, CDN1163 alone has no significant effects on the Ca2+-dependent shifts in the structural populations of sarcoplasmic reticulum Ca2+-ATPase, and it does not increase the pump's affinity for Ca2+ ions. However, we found that CDN1163 enhances the ATP-mediated modulatory effects to increase the population of catalytically competent sarcoplasmic reticulum Ca2+-ATPase structures. Importantly, this structural shift occurs within the physiological window of Ca2+ concentrations at which sarcoplasmic reticulum Ca2+-ATPase operates. We demonstrated that ATP is both a substrate and modulator of sarcoplasmic reticulum Ca2+-ATPase and showed that CDN1163 and ATP act synergistically to populate sarcoplasmic reticulum Ca2+-ATPase structures that are primed for phosphorylation. This study provides novel insights into the structural mechanisms for sarcoplasmic reticulum Ca2+-ATPase activation by its substrate and a synthetic allosteric modulator.

The Current Status and Use of Microphysiological Systems by the Pharmaceutical Industry: The International Consortium for Innovation and Quality Microphysiological Systems Affiliate Survey and Commentary.

Microphysiological systems (MPS) are comprised of one or multiple cell types of human or animal origins that mimic the biochemical/electrical/mechanical responses and blood-tissue barrier properties of the cells observed within a complex organ. The goal of incorporating these in vitro systems is to expedite and advance the drug discovery and development paradigm with improved predictive and translational capabilities. Considering the industry need for improved efficiency and the broad challenges of model qualification and acceptance, the International Consortium for Innovation and Quality (IQ) founded an IQ MPS working group in 2014 and Affiliate in 2018. This group connects thought leaders and end users, provides a forum for crosspharma collaboration, and engages with regulators to qualify translationally relevant MPS models. To understand how pharmaceutical companies are using MPS, the IQ MPS Affiliate conducted two surveys in 2019, survey 1, and 2021, survey 2, which differed slightly in the scope of definition of the complex in vitro models under question. The surveys captured demographics, resourcing, rank order for organs of interest, compound modalities tested, and MPS organ-specific questions, including nonclinical species needs and cell types. The major focus of this manuscript is on results from survey 2, where we specifically highlight the context of use for MPS within safety, pharmacology, or absorption, disposition, metabolism, and excretion and discuss considerations for including MPS data in regulatory submissions. In summary, these data provide valuable insights for developers, regulators, and pharma, offering a view into current industry practices and future considerations while highlighting key challenges impacting MPS adoption. SIGNIFICANCE STATEMENT: The application of microphysiological systems (MPS) represents a growing area of interest in the drug discovery and development framework. This study surveyed 20+ pharma companies to understand resourcing, current areas of application, and the key challenges and barriers to internal MPS adoption. These results will provide regulators, tech providers, and pharma industry leaders a starting point to assess the current state of MPS applications along with key learnings to effectively realize the potential of MPS as an emerging technology.

Unlocking DCAFs To Catalyze Degrader Development: An Arena for Innovative Approaches.

Chemically induced proximity-based targeted protein degradation (TPD) has become a prominent paradigm in drug discovery. With the clinical benefit demonstrated by certain small-molecule protein degraders that target the cullin-RING E3 ubiquitin ligases (CRLs), the field has proactively strategized to tackle anticipated drug resistance by harnessing additional E3 ubiquitin ligases to enrich the arsenal of this therapeutic approach. Here, we endeavor to explore the collaborative efforts involved in unlocking a broad range of CRL4DCAF for degrader drug development. Throughout the discussion, we also highlight how both conventional and innovative approaches in drug discovery can be taken to realize this objective. Moving ahead, we expect a greater allocation of resources in TPD to pursue these high-hanging fruits.

Network pharmacology-based findings of the immunomodulatory activity of phytocompounds from Withania somnifera and Aloe barbadensis

Immunomodulation constitutes a crucial part of individual organisms&amp;rsquo; defense systems. Moreover, the utilization of plant-based natural products as herbal medicine for immunomodulation has garnered significant interest. Herein, we examined the immunomodulatory potentials of active phytocompounds extracted from Withania somnifera and Aloe barbadensis by employing ADMET screening, network pharmacology, and molecular docking techniques. This study follows the paradigm in drug discovery, which has shifted from a &amp;ldquo;one-target, one-drug&amp;rdquo; mode to a &amp;ldquo;network-target, multiple-component-therapeutics&amp;rdquo; mode. Phyto compounds sourced from W. somnifera and A. barbadensis were mined from online databases, including Dr. Duke&amp;rsquo;s Phytochemical Ethnobotanical Database. After screening these active compounds, their potential targets were predicted through in silico ADMET property prediction models. Network pharmacology was utilized to establish a &amp;ldquo;compound-protein/gene-disease&amp;rdquo; network and reveal the regulatory mechanism of small molecules in a high-throughput manner through STRING, Cytohubba plugin in Cytoscape, and the g: Profiler software. A molecular docking simulation was performed to examine the binding affinity between the selected hub targets and bioactives. The findings showed that phytocompounds derived from the W. somnifera and A. barbadensis exhibit immunomodulatory effects by inhibiting specific protein targets, notably AKT1, HCK, JAK2, PDPK1, KIT, and IL2. Molecular docking analysis further revealed the potential of withanolide G, somniferine, and somniferanolide as promising immunomodulatory compounds against HCK, JAK2, and PDPK1 proteins, which are involved in multiple myeloma pathways, encompassing the PI3K-Akt signaling pathway, NOD-like receptor signaling pathway, and Toll-like receptor signaling pathway. In conclusion, these compounds are recommended for further in vivo and in vitro investigations to ascertain their potential as treatments for multiple myeloma.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs.

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.