Early-stage Drug Research Articles

Studying Target-Engagement of Anti-Infectives by Solvent-Induced Protein Precipitation and Quantitative Mass Spectrometry.

Antimicrobial resistance (AMR) poses a serious threat to global health. The rapid emergence of resistance contrasts with the slow pace of antimicrobial development, emphasizing the urgent need for innovative drug discovery approaches. This study addresses a critical bottleneck in early drug development by introducing integral solvent-induced protein precipitation (iSPP) to rapidly assess the target-engagement of lead compounds in extracts of pathogenic microorganisms under close-to-physiological conditions. iSPP measures the change in protein stability against solvent-induced precipitation in the presence of ligands. The iSPP method for bacteria builds upon established SPP procedures and features optimized denaturation gradients and minimized sample input amounts. The effectiveness of the iSPP workflow was initially demonstrated through a multidrug target-engagement study. Using quantitative mass spectrometry (LC-MS/MS), we successfully identified known drug targets of seven different antibiotics in cell extracts of four AMR-related pathogens: the three Gram-negative bacteria Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and the Gram-positive bacterium Staphylococcus aureus. The iSPP method was ultimately applied to demonstrate target-engagement of compounds derived from target-based drug discovery. We employed five small molecules targeting three enzymes in the 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway─a promising focus for anti-infective drug development. The study showcases iSPP adaptability and efficiency in identifying anti-infective drug targets, advancing early-stage drug discovery against AMR.

How organ-on-a-chip is advancing cancer research and oncology - a cancer hallmarks’ perspective

Despite significant improvements in methodologies behind the development of new anticancer therapies, the path from early-stage drug development through preclinical and clinical development pipeline is still arduous. Further refinement of the current preclinical models and the development of complementing alternative techniques that enable more reliable studies are paramount. The emerging organ-on-a-chip (OoC) technologies are physiological-like organ biomimetic systems built on a microfluidic chip, capable of enabling precise control over various physicochemical and biomechanical parameters and helping recreate the natural physiology and mechanical forces that cells experience in the human body. In oncology research, especially, since cancer has been understood to be a dynamic disease featured by complex interactions between cancer cells and their environment, this technology offers added advantage as it is able to provide a dynamic platform to simulate cancer-on-a-chip emulating the biological context of tumor microenvironment (TME); demonstrating progression to metastases to multiorgans; and helping to unravel complex information that other current in vitro methods are otherwise not able to provide. In this contribution, we provide a biological perspective on the recent advances in the field of OoC models in cancer biology studies from a cancer hallmark perspective. We also discuss current prospects and funding opportunities in this space, as well as a possible future outlook from a biology perspective, including major challenges and new opportunities as a way forward to OoC technologies.

Identification of Potential Inhibitors of Mycobacterium tuberculosis Amidases: An Integrated In Silico and Experimental Study.

Virtual screening is a crucial tool in early stage drug discovery for identifying potential hit candidates. Here, we present an integrated approach that combines theoretical and experimental techniques to identify, for the first time, inhibitors of amidases (Ami1-Ami4) from Mycobacterium tuberculosis. Through computational methods, we proposed a set of potential inhibitors, which were subsequently evaluated experimentally using differential scanning fluorimetry. This led to the identification of two promising hits: a carbohydrazide core (hit 1) and a tetrazole core (hit 5). We further developed a small collection of compounds derived from hit 1, which demonstrated improved affinity for Ami1. Additionally, we determined the crystallographic structure of the Ami1-hit 5 complex at a resolution of 1.45 Å, providing molecular-level insights into the interaction of this compound within the catalytic site. The findings of this study contribute to the advancement of drug discovery against tuberculosis and propose new targets for therapeutic development.

NeuroClick: Advanced Software for Designing Blood-Brain Barrier-Permeable Drugs Using Click Reaction Simulations.

NeuroClick is a software tool designed for the in silico execution of azide-alkyne cycloaddition reactions, commonly known as click reactions. We developed this graphical user interface application to expedite the drug discovery process by generating libraries of 1,2,3-triazole compounds. NeuroClick enables users to input reagent SMILES strings, rapidly generating and screening extensive combinatorial libraries at a pace of 10,000 molecules per minute. The software applies stringent criteria to ensure the relevance and accuracy of the generated compounds, excluding molecules without azide groups or those with multiple reactive functional groups to maintain dataset integrity. NeuroClick incorporates advanced filtering options based on Lipinski's rule of five and blood-brain barrier (BBB) permeability predictors, allowing researchers to identify drug-like molecules with potential central nervous system activity. The software's high-throughput and user-friendly interface significantly enhance the efficiency of early-stage drug development by facilitating the exploration of vast chemical spaces and identifying promising lead compounds for further development. This article provides comprehensive guidance on the installation, usage, and features of NeuroClick, ensuring that users can leverage its full potential in their research endeavors. © 2024 Wiley Periodicals LLC. Basic Protocol: NeuroClick workflow for generating BBB-permeating drugs.

Use of Nanotechnology in Cosmetics and Cosmeceuticals

Nanotechnology has revolutionized the cosmetic and cosmeceutical industries by enhancing product efficacy and enabling innovative formulations. This review paper delves into the various applications of nanotechnology in skincare and cosmetic products, highlighting its ability to improve the delivery of active ingredients, provide enhanced protection, and increase the bioavailability of beneficial compounds. Nanomaterials such as liposomes, nanoemulsions, nanocapsules, and solid lipid nanoparticles are now extensively utilized for their superior skin penetration capabilities, ensuring targeted delivery and prolonged effects. The paper explores the potential benefits and safety concerns associated with these nano-based cosmetic products. It also provides a comprehensive analysis of recent advancements, regulatory considerations, and future trends in the field, emphasizing the need for further research to fully understand the long-term implications of nanotechnology in cosmetics and cosmeceuticals. This review aims to provide a critical overview for researchers and industry professionals interested in the evolving landscape of nanotechnology in beauty and skincare. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

Widespread loss-of-function mutations implicating preexisting resistance to new or repurposed anti-tuberculosis drugs

BackgroundFive New or Repurposed Drugs (NRDs) were approved in the last decade for treatment of multi-drug resistant tuberculosis: bedaquiline, clofazimine, linezolid, delamanid, and pretomanid. Unfortunately, resistance to these drugs emerged faster than anticipated, potentially due to preexisting resistance in naïve strains. Previous investigations into the rapid emergence have mostly included short variants. For the first time, we utilize de novo-assembled genomes, and systematically include Structural Variations (SV) and heterogeneity to comprehensively study this rapid emergence. We show high prevalence of preexisting resistance, identify novel markers of resistance, and lay the foundation for preventing preexisting resistance in future drug development. MethodsFirst, a systematic literature review revealed 313 NRD resistance variants in 13 genes. Next, 409 globally diverse clinical isolates collected prior to the drugs’ programmatic use (308 were multidrug resistant, 106 had de novo assembled genomes) were utilized to study the 13 genes comprehensively for conventional, structural, and heterogeneous variants. FindingsWe identified 5 previously reported and 67 novel putative NRD resistance variants. These variants were 2 promoter mutations (in 8/409 isolates), 13 frameshifts (21/409), 6 SVs (9/409), 35 heterogeneous frameshifts (32/409) and 11 heterogeneous SVs (12/106). Delamanid and pretomanid resistance mutations were most prevalent (48/409), while linezolid resistance mutations were least prevalent (8/409). InterpretationPreexisting mutations implicated in resistance to at least one NRD was highly prevalent (85/409, 21 %). This was mostly caused by loss-of-function mutations in genes responsible for prodrug activation and efflux pump regulation. These preexisting mutations may have emerged through a bet-hedging strategy, or through cross-resistance with non-tuberculosis drugs such as metronidazole. Future drugs that could be resisted through loss-of-function in non-essential genes may suffer from preexisting resistance. The methods used here for comprehensive preexisting resistance assessment (especially SVs and heterogeneity) may mitigate this risk during early-stage drug development.

ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillation

Recent studies showed that the likelihood of drug approval can be predicted with clinical data and structure information of drug using computational approaches. Predicting the likelihood of drug approval can be innovative and of high impact. However, models that leverage clinical data are applicable only in clinical stages, which is not very practical. Prioritizing drug candidates and early-stage decision-making in the de novo drug development process is crucial in pharmaceutical research to optimize resource allocation. For early-stage decision-making, we need a computational model that uses only chemical structures. This seemingly impossible task may utilize the predictive power with multi-modal features including clinical data. In this work, we introduce ChemAP (Chemical structure-based drug Approval Predictor), a novel deep learning scheme for drug approval prediction in the early-stage drug discovery phase. ChemAP aims to enhance the possibility of early-stage decision-making by enriching semantic knowledge to fill in the gap between multi-modal and single-modal chemical spaces through knowledge distillation techniques. This approach facilitates the effective construction of chemical space solely from chemical structure data, guided by multi-modal knowledge related to efficacy, such as clinical trials and patents of drugs. In this study, ChemAP achieved state-of-the-art performance, outperforming both traditional machine learning and deep learning models in drug approval prediction, with AUROC and AUPRC scores of 0.782 and 0.842 respectively on the drug approval benchmark dataset. Additionally, we demonstrated its generalizability by outperforming baseline models on a recent external dataset, which included drugs from the 2023 FDA-approved list and the 2024 clinical trial failure drug list, achieving AUROC and AUPRC scores of 0.694 and 0.851. These results demonstrate that ChemAP is an effective method in predicting drug approval only with chemical structure information of drug so that decision-making can be done at the early stages of drug development process. To the best of our knowledge, our work is the first of its kind to show that prediction of drug approval is possible only with structure information of drug by defining the chemical space of approved and unapproved drugs using deep learning technology.

Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery

Inflammatory diseases such as asthma, rheumatoid arthritis, and cardiovascular conditions are driven by overproduction of leukotriene B4 (LTB4), a potent inflammatory mediator. Leukotriene A4 hydrolase (LTA4H) plays a critical role in converting leukotriene A4 into LTB4, making it a prime target for drug discovery. Despite ongoing efforts, developing effective LTA4H inhibitors has been challenging due to the complex binding properties of the enzyme and the structural diversity of potential inhibitors. Traditional drug discovery methods, like high-throughput screening (HTS), are often time-consuming and inefficient, prompting the need for more advanced approaches. Quantitative Structure-Activity Relationship (QSAR) modeling, enhanced by ensemble machine learning techniques, provides a promising solution by enabling accurate prediction of compound bioactivity based on molecular descriptors. In this study, six ensemble machine learning methods—AdaBoost, Extra Trees, Gradient Boosting, LightGBM, Random Forest, and XGBoost—were employed to classify LTA4H inhibitors. The dataset, comprising 636 compounds labeled as active or inactive based on pIC50 values, was processed to extract 450 molecular descriptors after feature engineering. The results show that the LightGBM model achieved the highest classification accuracy (83.59%) and Area Under the Curve (AUC) value (0.901), outperforming other models. XGBoost and Random Forest also demonstrated strong performance, with AUC values of 0.890 and 0.895, respectively. The high sensitivity (95.24%) of the XGBoost model highlights its ability to accurately identify active compounds, though it exhibited slightly lower specificity (61.36%), indicating a higher false-positive rate. These findings suggest that ensemble machine learning models, particularly LightGBM, are highly effective in predicting bioactivity, offering valuable tools for early-stage drug discovery. The results indicate that ensemble methods significantly enhance QSAR model accuracy, making them viable for identifying promising LTA4H inhibitors, potentially accelerating the development of anti-inflammatory therapies.

Transforming Drug Discovery: The Impact of Artificial Intelligence and Machine Learning from Initial Screening to Clinical Trials

Abstract: The landscape of drug discovery is undergoing a profound transformation driven by the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This paper explores the impact of these advancements on various stages of the drug discovery process, from initial screening to clinical trials. Initially, AI and ML are revolutionizing the early stages of drug discovery by enhancing the efficiency of target identification and lead compound screening. Traditional methods, which often rely on labor-intensive processes and high costs, are being supplemented with AI-driven algorithms that analyze vast datasets to identify potential drug targets and predict the biological activity of compounds with unprecedented accuracy. In the drug design and optimization phase, ML models facilitate the prediction of drug interactions and side effects, thus accelerating the development of safer and more effective therapeutics. Advanced simulations and predictive models reduce the reliance on experimental trials, thereby streamlining the development pipeline. The clinical trials phase also benefits significantly from AI and ML. These technologies improve patient stratification by identifying suitable candidates based on genetic and clinical data, optimizing trial designs, and predicting patient responses to treatment. This not only enhances the efficiency of clinical trials but also increases the likelihood of successful outcome. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.

Rapid, Accurate, Ranking of Protein-Ligand Binding Affinities with VM2, the Second-Generation Mining Minima Method.

The structure-based technologies most widely used to rank the affinities of candidate small molecule drugs for proteins range from faster but less reliable docking methods to slower but more accurate explicit solvent free energy methods. In recent years, we have advanced another technology, which is called mining minima because it "mines" out the main contributions to the chemical potentials of the free and bound molecular species by identifying and characterizing their main local energy minima. The present study provides systematic benchmarks of the accuracy and computational speed of mining minima, as implemented in the VeraChem Mining Minima Generation 2 (VM2) code, across two well-regarded protein-ligand benchmark data sets, for which there are already benchmark data for docking, free energy, and other computational methods. A core result is that VM2's accuracy approaches that of explicit solvent free energy methods at a far lower computational cost. In finer-grained analyses, we also examine the influence of various run settings, such as the treatment of crystallographic water molecules, on the accuracy, and define the costs in time and dollars of representative runs on Amazon Web Services (AWS) compute instances with various CPU and GPU combinations. We also use the benchmark data to determine the importance of VM2's correction from generalized Born to finite-difference Poisson-Boltzmann results for each energy well and find that this correction affords a remarkably consistent improvement in accuracy at a modest computational cost. The present results establish VM2 as a distinctive technology for early-stage drug discovery, which provides a strong combination of efficiency and predictivity.

Revolutionizing Healthcare and Drug Discovery: The Impact of Artificial Intelligence on Pharmaceutical Development

Artificial intelligence (AI) is reshaping drug discovery and delivery in the pharmaceutical industry, fundamentally transforming traditional methods. In drug discovery, AI algorithms rapidly analyze vast biological and chemical datasets to identify potential drug candidates with unprecedented accuracy. Machine learning models predict compound efficacy and safety, accelerating earlystage drug development. AI also facilitates drug repurposing, uncovering new therapeutic uses for existing medications. At the drug delivery front, AI optimizes formulations and systems, enabling targeted and personalized approaches. Intelligent algorithms enhance the understanding of pharmacokinetics and pharmacodynamics, guiding the development of precision medicine strategies. This integration of AI not only expedites innovative drug discovery but also refines delivery mechanisms, promising more effective and tailored treatments with the potential to revolutionize patient care. The data-processing capabilities of AI drive digitalization and widespread utilization. Applications in drug discovery, development, repurposing, and clinical trials aim to alleviate human workload, expedite objectives, and foster innovation. Despite promising prospects, concerns about job displacement and stringent regulations accompany AI implementation. Emphasizing the intent to augment human labor rather than replace it entirely, the industry anticipates that AI will become a pivotal resource, propelling efficiency, innovation, and advancements in healthcare. This review emphasizes the role of AI in transforming drug discovery and delivery

Daniel H. Wolf: Understanding motivation impairment from clinical, behavioral, and neurobiological perspectives to pave the way for better treatments

Dr. Daniel Wolf grew up in Los Angeles, completed his BA at Harvard College, MD-PhD at Yale University, psychiatry residency at MGH-McLean, and neuropsychiatry fellowship at UPenn. He is an Associate Professor of Psychiatry at UPenn's Perelman School of Medicine. As head of the Laboratory for Motivation in Psychiatry, Dr. Wolf uses functional neuroimaging to study the neural mechanisms of amotivation and other symptom dimensions in psychosis and at-risk states, aiming to develop novel assessment biomarkers for early-stage drug development. Dr. Wolf also provides outpatient care to individuals with psychosis while supervising clinical trainees. As Director of the Clinical Neurosciences Training Program and Co-Director of the Psychosis T32, he provides seminars and mentoring to medical and graduate students and postdoctoral fellows; he earned the School of Medicine's teaching awards in both Basic Science and Translational Science. He is also an ACNP Fellow and member of the Neuropsychopharmacology editorial board. Dr. Wolf answered the Genomic Press Interview, providing our readers with reflections on his life and career.

CNS Drug Discovery in Academia: Where Basic Research Meets Innovation.

The involvement of academic research in drug discovery is consistently growing. However, academic projects seldom advance to clinical trials. Here, we assess the landscape of drug discovery within the National Centre of Competence in Research (NCCR) TransCure launched by the Swiss National Science Foundation to foster basic research and early-stage drug discovery on membrane transporters. This included transporters in central nervous system (CNS) disorders, which represent a huge unmet medical need. While idea championship, sustainable funding, collaborations between disciplines at the interface of academia and industry are important for translational research, Popperian falsifiability, strong intellectual property and a motivated startup team are key elements for innovation. This is exemplified by the NCCR TransCure spin-off company Synendos Therapeutics, a clinical stage biotech company developing the first selective endocannabinoid reuptake inhibitors (SERIs) as novel treatment for neuropsychiatric disorders. We provide a perspective on the challenges related to entering an uncharted druggable space and bridging the often mentioned "valley of death". The high attrition rate of drug discovery projects in the CNS field within academia is often due to the lack of meaningful animal models that can provide pharmacological proof-of-concept for potentially disruptive technologies at the earliest stages, and the absence of solid intellectual property.

Ligand-Based Compound Activity Prediction via Few-Shot Learning.

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a "few-shot learning" challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets. The code for FS-CAP is available at https://github.com/Rose-STL-Lab/FS-CAP.

Rapid Protein-Ligand Affinity Determination by Photoinduced Hyperpolarized NMR.

The binding affinity determination of protein-ligand complexes is a cornerstone of drug design. State-of-the-art techniques are limited by lengthy and expensive processes. Building upon our recently introduced novel screening method utilizing photochemically induced dynamic nuclear polarization (photo-CIDNP) NMR, we provide the methodological framework to determine binding affinities within 5-15 min using 0.1 mg of protein. The accuracy of our method is demonstrated for the affinity constants of peptides binding to a PDZ domain and fragment ligands binding to the protein PIN1. The method can also be extended to measure the affinity of nonphoto-CIDNP-polarizable ligands in competition binding experiments. Finally, we demonstrate a strong correlation between the ligand-reduced signals in photo-CIDNP-based NMR fragment screening and the well-established saturation transfer difference (STD) NMR. Thus, our methodology measures protein-ligand affinities in the micro- to millimolar range in only a few minutes and informs on the binding epitope in a single-scan experiment, opening new avenues for early stage drug discovery approaches.

The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening

Computationally screening chemical libraries to discover molecules with desired properties is a common technique used in early-stage drug discovery. Recent progress in the field now enables the efficient exploration of billions of molecules within days or hours, but this exploration remains confined within the boundaries of the accessible chemistry space. While the number of commercially available compounds grows rapidly, it remains a limited subset of all druglike small molecules that could be synthesized. Here, we present a workflow where chemical reactions typically developed in academia and unconventional in drug discovery are exploited to dramatically expand the chemistry space accessible to virtual screening. We use this process to generate a first version of the Pan-Canadian Chemical Library, a collection of nearly 150 billion diverse compounds that does not overlap with other ultra-large libraries such as Enamine REAL or SAVI and could be a resource of choice for protein targets where other libraries have failed to deliver bioactive molecules.

Exploring the anticancer properties of Azadirachta indica: In silico and in vitro study of its phytochemicals against hepatocellular carcinoma

The medicinal plant Azadirachta indica (Neem) interferes with carcinogenesis and acts as a tumor suppressor. This study evaluated the anticancer effects of a methanol extract of Azadirachta Indica on a human hepatocellular carcinoma cell line (HepG2) using an in vitro MTT assay. The results demonstrated that the methanol extract of Azadirachta Indica exhibited significant cytotoxicity against HepG2 cells, with an IC₅₀ value of 81.17 µg/ml. Furthermore, an in silico approach was used to investigate the interaction between Azadirachta indica and Akt1, a protein kinase implicated in the pathogenesis of hepatocellular carcinoma (HCC) via the PI3K/Akt/mTOR pathway. This study identified two promising Akt1 inhibitors, 2′,3′-dehydrosalannol and quercetin, with improved binding affinity (ΔGbind) and a safe pharmacological profile. These compounds also showed hepatoprotective effects. Molecular dynamics simulations, specifically the MM-GBSA model, were used to further scrutinize the inhibitory potential of the docked bioconstituents. The results suggest that the proposed in silico pipeline can be useful for identifying lead candidates for HCC through early-stage rational drug design. These candidates can then be verified in vitro and in vivo before being tested in clinical trials as mainstream targets against HCC.

The Ugi4CR as effective tool to access promising anticancer isatin-based α-acetamide carboxamide oxindole hybrids.

Considering early-stage drug discovery programs, the Ugi four-component reaction is a valuable, flexible, and pivotal tool, facilitating the creation of two new amide bonds in a one-pot fashion to effectively yield the desired α-aminoacylamides. Here, we highlight the reputation of this reaction approach to access number and scaffold diversity of a library of isatin-based α-acetamide carboxamide oxindole hybrids, promising anticancer agents, in a mild and fast sustainable reaction process. The library was tested against six human solid tumor cell lines, among them, non-small cell lung carcinoma, cervical adenocarcinoma, breast cancer and colon adenocarcinoma. The most potent compounds 8d, 8h and 8k showed GI50 values in the range of 1-10 μM.

MolModa: accessible and secure molecular docking in a web browser.

Molecular docking advances early-stage drug discovery by predicting the geometries and affinities of small-molecule compounds bound to drug-target receptors, predictions that researchers can leverage in prioritizing drug candidates for experimental testing. Unfortunately, existing docking tools often suffer from poor usability, data security, and maintainability, limiting broader adoption. Additionally, the complexity of the docking process, which requires users to execute a series of specialized steps, often poses a substantial barrier for non-expert users. Here, we introduce MolModa, a secure, accessible environment where users can perform molecular docking entirely in their web browsers. We provide two case studies that illustrate how MolModa provides valuable biological insights. We further compare MolModa to other docking tools to highlight its strengths and limitations. MolModa is available free of charge for academic and commercial use, without login or registration, at https://durrantlab.com/molmoda.