Stages Of Drug Discovery Research Articles

Future Directions for Quantitative Systems Pharmacology.

In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI. In preclinical development, QSP will integrate with non-animal "new approach methodologies" and reverse-translated datasets to improve understanding of and translation to the human patient. During clinical development, integration with complementary modeling approaches and multimodal patient data will create multidimensional digital twins and virtual populations for clinical trial simulations that guide clinical development and point to opportunities for precision medicine. QSP can evolve into this future by (1) pursuing high-impact applications enabled by novel experimental and quantitative technologies and data types; (2) integrating closely with analytical and computational advancements; and (3) increasing efficiencies through automation, standardization, and model reuse. In this vision, the QSP expert will play a critical role in designing strategies, evaluating data, staging and executing analyses, verifying, interpreting, and communicating findings, and ensuring the ethical, safe, and rational application of novel data types, technologies, and advanced analytics including AI/ML.

Relational Graph Convolution Network with Multi Features for Anti- COVID-19 Drugs Discovery using 3CLpro Potential Target

Background: The potential of graph neural networks (GNNs) to revolutionize the analysis of non-Euclidean data has gained attention recently, making them attractive models for deep machine learning. However, insufficient compound or molecular graphs and feature representations might significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing COVID-19 across the globe, for which there is no drug with proven efficacy that has been shown to be effective. As various stages of drug discovery and repositioning require the accurate prediction of drug-target interactions (DTI), here, we propose a relational graph convolution network (RGCN) using multi-features based on the developed drug compound-coronavirus target graph data representation and combination of features. During the implementation of the model, we further introduced the use of not only the feature module to understand the topological structure of drugs but also the structure of the proven drug target (i.e., 3CLpro) for SARS-CoV-2 that shares a genome sequence similar to that of other members of the betacoronavirus group such as SARS-CoV, MERS-CoV, bat coronavirus. Our feature comprises topological information in molecular SMILES and local chemical context in the SMILES sequence for the drug compound and drug target. Our proposed method prevailed with high and compelling performance accuracy of 97.30% which could be prioritized as the potential and promising prediction route for the development of novel oral antiviral medicine for COVID- 19 drugs. Objective: Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational methods in DTI prediction has intensified due to the considerable expense and time investment associated with conducting extensive in vitro and in vivo experiments. Machine learning (ML) techniques, particularly deep learning, have found broad applications in DTI prediction. We are convinced that this study could be prioritized and utilized as the promising predictive route for the development of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses. Methods: This study addressed the problem of COVID-19 drugs using proposed RGCN with multifeatures as an attractive and potential route. This study focused mainly on the prediction of novel antiviral drugs against coronaviruses using graph-based methodology, namely RGCN. This research further utilized the features of both drugs and common potential drug targets found in betacoronaviruses group to deepen understanding of their underlying relation. Results: Our suggested approach prevailed with a high and convincing performance accuracy of 97.30%, which may be utilized as a top priority to support and advance this field in the prediction and development of novel antiviral treatments against coronaviruses and their variants. Conclusion: We recursively performed experiments using the proposed method on our constructed DCCCvT graph dataset from our collected dataset with various single and multiple combinations of features and found that our model had achieved comparable best-averaged accuracy performance on T7 features followed by a combination of T7, R6, and L8. The proposed model implemented in this investigation turns out to outperform the previous related works.

Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage.

A force field is a critical component in molecular dynamics simulations for computational drug discovery. It must achieve high accuracy within the constraints of molecular mechanics' (MM) limited functional forms, which offers high computational efficiency. With the rapid expansion of synthetically accessible chemical space, traditional look-up table approaches face significant challenges. In this study, we address this issue using a modern data-driven approach, developing ByteFF, an Amber-compatible force field for drug-like molecules. To create ByteFF, we generated an expansive and highly diverse molecular dataset at the B3LYP-D3(BJ)/DZVP level of theory. This dataset includes 2.4 million optimized molecular fragment geometries with analytical Hessian matrices, along with 3.2 million torsion profiles. We then trained an edge-augmented, symmetry-preserving molecular graph neural network (GNN) on this dataset, employing a carefully optimized training strategy. Our model predicts all bonded and non-bonded MM force field parameters for drug-like molecules simultaneously across a broad chemical space. ByteFF demonstrates state-of-the-art performance on various benchmark datasets, excelling in predicting relaxed geometries, torsional energy profiles, and conformational energies and forces. Its exceptional accuracy and expansive chemical space coverage make ByteFF a valuable tool for multiple stages of computational drug discovery.

Role of Computer-Aided Design and Development in Modern Pharmaceuticals

The integration of molecular biology and computational technologies has transformed modern drug discovery and development processes. Advanced computational methodologies, particularly artificial intelligence (AI) and machine learning (ML), have reshaped traditional drug development approaches through unprecedented access to ligand property data, target binding interactions, three-dimensional protein structures, and virtual libraries containing billions of drug-like molecules. AI and deep learning (DL) implementations have enhanced multiple stages of drug discovery, from target identification to lead optimization. These computational advances, backed by improved hardware capabilities and sophisticated algorithms, now enable targeting of previously "undruggable" proteins. This review presents modern computational approaches in pharmaceutical development, including strategies for challenging protein targets through covalent regulation, allosteric inhibition, protein-protein interaction modulation, and targeted protein degradation. AI-driven methods have accelerated drug discovery pipelines, reduced development costs, and improved clinical trial success rates. The transition from traditional broad-spectrum approaches to precision medicine, supported by computational tools, has enabled personalized therapeutic strategies. Current limitations in computer-aided drug design persist, yet the combination of computational predictions with experimental validation continues to advance therapeutic development. Recent developments in quantum computing and advanced neural networks promise to further enhance drug discovery efficiency and success rates in the coming decades.

Rapid discovery and optimization of novel brain shuttles and shuttle‐enabled anti‐amyloid therapeutics using multiplexed in vivo biologics design

AbstractBackgroundDrug delivery to the central nervous system has long been hindered by the restrictive properties of the blood‐brain barrier (BBB). Recent advances have highlighted the ability of certain antibody “shuttles'' to traverse the BBB and enhance the delivery of diverse therapeutic payloads. However the true potential of this approach remains underexplored and is limited by traditional in vitro screening platforms that fail to capture the complexities of in vivo biology. Novel technologies that facilitate the development of brain shuttles and shuttle‐enabled neuromedicines could improve the uptake, efficacy, safety, and dosing of broad classes of therapeutics, and transform the treatment of vast numbers of patients with neurological disease.MethodHere, we introduce a high‐throughput in vivo screening methodology based on Manifold’s mCodeTM technology to evaluate BBB penetration and uptake of over 1000 brain shuttling antibody candidates targeting 10+ different receptors (Figure).ResultOur approach reveals antibody shuttles with diverse molecular properties and distinct pharmacokinetic profiles in brain, blood, and other peripheral tissues. We leverage this large in vivo dataset using machine learning to further engineer these antibodies for enhanced brain uptake and residence time. Engineering of brain shuttles with anti‐amyloid antibodies against BACE1 and amyloid beta significantly improved the uptake and efficacy of these fusions versus unshuttled controls and comparator shuttled molecules.ConclusionCollectively, our work (1) expands the pool of known targets and molecules capable of shuttling therapeutic payloads across the BBB, (2) validates the utility of these molecules toward the design of novel neuromedicines for Alzheimer’s disease, and (3) signifies a paradigm shift in utilizing thoughtfully‐designed, high‐throughput in vivo screening in early stages of drug discovery.

Improved and Interpretable Prediction of Cytochrome P450-Mediated Metabolism by Molecule-Level Graph Modeling and Subgraph Information Bottlenecks.

Accurately identifying sites of metabolism (SoM) mediated by cytochrome P450 (CYP) enzymes, which are responsible for drug metabolism in the body, is critical in the early stage of drug discovery and development. Current computational methods for CYP-mediated SoM prediction face several challenges, including limitations to traditional machine learning models at the atomic level, heavy reliance on complex feature engineering, and the lack of interpretability relevant to medicinal chemistry. Here, we propose GraphCySoM, a novel molecule-level modeling approach based on graph neural networks, utilizing lightweight features and interpretable annotations on substructures, to effectively and interpretably predict CYP-mediated SoM. Unlike computationally expensive atomic descriptors derived from resource-intensive chemistry or even quantum chemistry calculations, we emphasize that graph-based molecular modeling initialized solely with lightweight features enables the adaptive learning of molecular topology through message-passing mechanisms combined with various aggregation kernels. Extensive ablation experiments demonstrate that GraphCySoM significantly outperforms baseline models and achieves superior performance compared with competing methods while exhibiting advantages in computational efficiency. Moreover, the attention mechanism and subgraph information bottlenecks are incorporated to analyze node importance and feature significance, resulting in mining substructures associated with the SoM. To the best of our knowledge, this is the first comprehensive study of CYP-mediated SoM using molecule-level modeling and interpretable technology. Our method achieves new state-of-the-art performance and provides potential insights into the molecular and pharmacological mechanisms underlying drug metabolism catalyzed by CYP enzymes. All source files and trained models are freely available at https://github.com/liyigerry/GraphCySoM.

Identifying selective PDHK inhibitors using coupled tensor matrix completion and experimental validation

Drug discovery often involves targeting specific members within a family of similar proteins. For example, pyruvate dehydrogenase kinase (PDHK) exists as four isozymes, which exhibit varying expression patterns across multiple tissues. Different PDHK isozymes have been implicated in conditions such as cancer, heart failure, and diabetes, suggesting that targeting them with inhibitors may offer therapeutic benefits. However, simultaneous inhibition of all four PDHK isozymes has the potential to be counterproductive, or poorly tolerated, highlighting a need for isoform-selective PDHK inhibitors. Despite multiple prior reports of PDHK inhibitors, identifying isoform-specific inhibitors for each PDHK isozyme has proven elusive. In this work, we propose a comprehensive framework that combines a machine learning-based prediction method and biochemical testing to screen a library of novel and previously reported compounds, thereby identifying selective inhibitors for PDHK isozymes. Initially, a coupled tensor matrix completion (CTMC) approach is employed to predict compound-target interaction (CTI) and pinpoint target-specific inhibitors. Subsequently, biochemical testing is performed to validate the predicted CTIs. Utilizing this approach, we successfully identified five novel PDHK1-specific inhibitors, underscoring the reliability of this approach as a screening method in the early stages of drug discovery and hit identification.

Probing antimicrobial synergy by novel lipopeptides paired with antibiotics

Antimicrobial resistance (AMR) is fast becoming a major global challenge in both hospital and community settings as many current antibiotics and treatment processes are under the threat of being rendered less effective or ineffective. Synergistic combination of an antibiotic and an aiding agent with a different set of properties provides an important but largely unexploited option to ‘repurpose’ existing biomaterial’s space while addressing issues of potency, spectrum, toxicity and resistance in early stages of antimicrobial drug discovery. This work explores how to combine tetracycline/minocycline (TC/MC) with a broad-spectrum antimicrobial lipopeptide that has been designed to improve the efficiency of membrane targeting and intramembrane accumulation, thereby enhancing antimicrobial efficacy. Experimental measurements of fractional inhibition concentration index (FICI) were undertaken from binary antibiotic-lipopeptide combinations. Most FICI values were found to be lower than 0.5 against both Gram-positive and Gram-negative bacterial strains studied including 3 AMR strains, revealing strong synergetic effects via favorable membrane-lytic interactions. The antimicrobial actions of this type of binary combinations are featured by the fast time-killing and high TC/MC uptake, benefited from effective membrane-lytic disruptions by the lipopeptide. This study thus provides an important mechanistic understanding of the combined antibiotic-lipopeptide approach to improve the therapeutic potential of conventional antibiotics by illustrating how amphiphilic lipopeptide-antibiotic combinations interact with biological membranes, providing a promising alternative to combat AMR through rational design of lipopeptide as an aiding agent.

Input Pose is Key to Performance of Free Energy Perturbation: Benchmarking with Monoacylglycerol Lipase.

Free energy perturbation (FEP) methodologies have become commonplace methods for modeling potency in hit-to-lead and lead optimization stages of drug discovery. The conformational states of the initial poses of compounds for FEP+ calculations are often set up by alignment to a cocrystal structure ligand, but it is not clear if this method provides the best result for all proteins or all ligands. Not only are ligand conformational states potential variables in modeling compound potency in FEP but also the selection of crystallographic water molecules for inclusion in the FEP input structures can impact FEP models. Here, we report the results of FEP calculations using FEP+ from Schrödinger and starting from maximum common substructure alignment and docked poses generated with an array of docking methodologies. As a benchmark data set, we use monoacylglycerol lipase (MAGL), an important clinical drug target in cancer malignancy, neurological diseases, and metabolic disorders, and a set of 17 MAGL inhibitors. We found a large variation among FEP+ correlations to experimental IC50 values depending on the method used to generate the input pose and that the inclusion of ligand-based information in the docking process, with some methods, increases the correlation between FEP+ free energies and IC50 values. Upon analysis of the initial poses, we found that the differences in FEP+ correlations stemmed from rotation around a tertiary amide bond as well as translation of the compound toward the more hydrophobic side of the MAGL pocket. FEP+ estimation improved across all pose modeling methods when hydrogen bond constraint information was added. However, simple maximum common substructure alignment in the presence of all crystallographic water molecules outperformed all other methods in correlation between estimated and experimental IC50 values. Taken together, these findings suggest that pose selection and crystallographic water inclusion greatly impact how well FEP+ estimated IC50 values align with experimental IC50 values and that modelers should benchmark a few different pose generation methodologies and different water inclusion strategies for their hit-to-lead and lead optimization drug discovery projects.

Applications of Lead Compounds in Daily Life

Abstract. Lead compounds are crucial in the early stages of drug discovery and development and play a pivotal role in pharmaceutical advancements. In addition to drug development, lead compounds have a myriad of other applications in daily life. This article reviews the extensive applications of lead compounds across various fields and explores their potential uses. First, this paper introduced the concept, characteristics, and research significance of lead compounds. Next, we discuss in detail the application of lead compounds in fields such as medicine, agriculture, food engineering, and material science, exploring their importance and potential trends in these areas. In the medical field, lead compounds provide a foundation for new drug development. Through the drug development process, compounds with potential therapeutic effects have been discovered and optimized, offering new avenues for disease treatment. In agriculture, lead compounds play a significant role in the development of pesticides and plant protection, helping to enhance crop yield, resilience, and quality and promoting sustainable agricultural practices. In the food industry, lead compounds are widely used in the development of food additives, functional foods, and food preservation technologies, enhancing the nutritional value and safety of food products. In the realm of material science, the application of lead compounds has spurred the research and development of new materials, improved material performance and functionality, and broadened their application fields. However, with continuous advancements in science and technology and evolving societal needs, research on lead compounds still faces many challenges and opportunities. Thus, strengthening the research and broader application of lead compounds will become a critical direction for future scientific endeavors.

Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1

BackgroundTarget identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches.ResultsWe synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development.ConclusionThis study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5’s HydraScreen and Strateos’ automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs.Scientific contributionWe present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.

DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model.

The analysis of protein-ligand binding sites plays a crucial role in the initial stages of drug discovery. Accurately predicting the ligand types that are likely to bind to protein-ligand binding sites enables more informed decision making in drug design. Our study, DeepLigType, determines protein-ligand binding sites using Fpocket and then predicts the ligand type of these pockets with the deep learning model, Convolutional Block Attention Module (CBAM) with ResNet. CBAM-ResNet has been trained to accurately predict five distinct ligand types. We classified protein-ligand binding sites into five different categories according to the type of response ligands cause when they bind to their target proteins, which are antagonist, agonist, activator, inhibitor, and others. We created a novel dataset, referred to as LigType5, from the widely recognized PDBbind and scPDB dataset for training and testing our model. While the literature mostly focuses on the specificity and characteristic analysis of protein binding sites by experimental (laboratory-based) methods, we propose a computational method with the DeepLigType architecture. DeepLigType demonstrated an accuracy of 74.30% and an AUC of 0.83 in ligand type prediction on a novel test dataset using the CBAM-ResNet deep learning model. For access to the code implementation of this research, please visit our GitHub repository at https://github.com/drorhunvural/DeepLigType.

HiMolformer: Integrating graph and sequence representations for predicting liver microsome stability with SMILES

In the initial stages of drug discovery or pre-clinical studies, understanding the metabolic stability of new molecules is crucial. Recently, research on pre-trained deep learning for molecular property prediction has been actively progressing, with various models being made open-source. However, most of these models rely on either 2D graph or 1D sequence for training, and the representation varies depending on the data format used. Consequently, combining multiple representations can broaden the scope of learning and may potentially be a manageable and most effective method to enhance performance.Therefore, we propose a novel hybrid model for predicting metabolic stability, which integrates representations from both graph-based and sequence-based models pre-trained for molecular features. This approach utilizes the combined strengths of 2D topological and 1D sequential information of molecules. HiMol, a graph-based graph neural network (GNN) model, and Molformer, a sequence-based Transformer model, were selected for integration, thus we named it HiMolformer. HiMolformer demonstrated superior performance compared to other models. We also focus on regression task for prediction with a empirical dataset from Korea Chemical Bank (KCB), comprising 3,498 molecules with mouse liver microsome (MLM) and human liver microsome (HLM) data obtained from actual metabolic reaction experiments. To the best of our knowledge, it is the first attempt to develop MLM and HLM prediction models using regression with a single SMILES input. The source code of this model is available at https://github.com/YUNSEOKWOO/HiMolformer.

Discovery of the Most Stable Form of an Adenosine Receptor Antagonist through Virtual Polymorph Screening and Targeted Crystallization

An innovative approach was developed to identify the optimal crystalline form, usually the thermodynamically most stable form. This method involves using virtual polymorph screening and targeted crystallization based on in silico solid-state modeling. By utilizing advanced crystal structure prediction (CSP) technology, the virtual polymorph screening method helps confirm whether the most stable crystalline form has been identified in actual crystallization experiments. If the predicted most stable form is not observed in experiments, predictions based on the method of COnductor like Screening MOdel for Real Solvents (COSMO-RS) are used to highlight solvent systems that can increase the likelihood of experimentally obtaining the desired form through a targeted crystallization process. In this work, such an approach has enabled the rapid discovery of the most stable polymorphic form and the development of a crystallization process of an adenosine receptor antagonist using minimal amounts of the sample within a shortened timeframe. Additionally, it provides a scientific rationale for ensuring the selection of the most stable form in the early stages of drug discovery, thereby reducing risks in future pharmaceutical development.

GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors

State‑of‑the‑art medical studies proved that predicting CYP450 enzyme inhibitors is beneficial in the early stage of drug discovery. However, accurate machine learning-based (ML) in silico methods for predicting CYP450 inhibitors remains challenging. Here, we introduce GTransCYPs, an improved graph neural network (GNN) with a transformer mechanism for predicting CYP450 inhibitors. This model significantly enhances the discrimination between inhibitors and non-inhibitors for five major CYP450 isozymes: 1A2, 2C9, 2C19, 2D6, and 3A4. GTransCYPs learns information patterns from molecular graphs by aggregating node and edge representations using a transformer. The GTransCYPs model utilizes transformer convolution layers to process features, followed by a global attention-pooling technique to synthesize the graph-level information. This information is then fed through successive linear layers for final output generation. Experimental results demonstrate that the GTransCYPs model achieved high performance, outperforming other state-of-the-art methods in CYP450 prediction.Scientific contributionThe prediction of CYP450 inhibition via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we presented a deep learning (DL) architecture based on GNN with transformer mechanism and attention pooling (GTransCYPs) to predict CYP450 inhibitors. Four GTransCYPs of different pooling technique were tested on an experimental tasks on the CYP450 prediction problem for the first time. Graph transformer with attention pooling algorithm achieved the best performances. Comparative and ablation experiments provide evidence of the efficacy of our proposed method in predicting CYP450 inhibitors. The source code is publicly available at https://github.com/zonwoo/GTransCYPs.

GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction.

In silico methods for prediction of chemical toxicity can decrease the cost and increase the efficiency in the early stage of drug discovery. However, due to low accessibility of sufficient and reliable toxicity data, constructing robust and accurate prediction models is challenging. Contrastive learning, a type of self-supervised learning, leverages large unlabeled data to obtain more expressive molecular representations, which can boost the prediction performance on downstream tasks. While molecular graph contrastive learning has gathered growing attentions, current models neglect the quality of negative data set. Here, we proposed a self-supervised pretraining deep learning framework named GCLmf. We first utilized molecular fragments that meet specific conditions as hard negative samples to boost the quality of the negative set and thus increase the difficulty of the proxy tasks during pre-training to learn informative representations. GCLmf has shown excellent predictive power on various molecular property benchmarks and demonstrates high performance in 33 toxicity tasks in comparison with multiple baselines. In addition, we further investigated the necessity of introducing hard negatives in model building and the impact of the proportion of hard negatives on the model.

A Cell-Free Kinetic Analysis of Ionizable Lipid Hydrolysis.

The prolonged retention of ionizable lipids within the body limits the repeated dosing of lipid nanoparticles (LNPs) for nucleic acid delivery. While most ionizable lipids are primarily metabolized in the liver via the enzymatic hydrolysis of ester bonds, elimination half-lives can range from several hours to days. The development of compounds that undergo rapid biodegradation remains a major engineering challenge in the absence of standardized biodegradability assessments in the early stages of drug discovery. Here, we analyze and compare the hydrolysis kinetics of well-known ionizable lipids (ALC-0315, DLin-MC3-DMA, LP-01, L319, and SM-102) using optimized cell-free reactions monitored by 1H NMR. Unlike conventional analytical techniques, these NMR-based methods are universal and suitable for high-throughput screening. We demonstrate that enzyme-catalyzed and base hydrolysis reactions can predict whether ionizable lipids undergo fast or slow liver elimination, as our results are in alignment with prior pharmacokinetic studies. Furthermore, we show that the hydrolysis kinetics of ionizable lipids vary by several orders of magnitude depending on steric effects. This study provides a framework to expedite the discovery of rapidly degradable ionizable lipids, with implications for improving the therapeutic index of LNP-based drugs.

In Vitro and In Silico Evaluation of Caffeic and Ferulic Acids Involvement in the Translocation of Glucose Transporter 4

Background: Insulin is a key hormone in our systems. Upon binding of insulin to its receptors in fat and muscle tissues, tens of proteins in the insulin signaling pathway are involved in the process of GLUT4 vesicle recruitment to the Plasma Membrane (PM) and the absorption of serum glucose. Deficits in the aforementioned pathway lead to insulin resistance and eventually to Type II Diabetes Mellitus. Objective: We appreciate the contribution of phytochemicals in the treatment of diabetes. Yet, in vitro and in silico studies are needed to validate the safety and efficacy of the phytochemicals, plus their action mechanisms. Methods: Herein, we tested two phytochemicals, caffeic acid and ferulic acid in vitro and in silico. We shed light on the insulin signaling proteins as plausible therapeutic targets using in silico studies, via AutoDock and SwissADME. Results: Results obtained in vitro indicate that Caffeic Acid (CA) increased GLUT4 translocation at 125μM by 31% in the absence of insulin, and 24.5% in presence of insulin, when compared to the control. Ferulic Acid (FA) was less potent as an enhancer of GLUT4 translocation. Best docking results were found for the binding of the phytochemicals CA and FA to PDK1, AKT, IRS1 and PTEN proteins of the insulin signaling, with comparable results. Conclusion: These findings indicate that CA and FA possess a limited anti-diabetic potency by increasing GLUT4 trafficking to the PM in skeletal muscles. These results suggest that these compounds are candidates for further investigation in pre-clinical and clinical stages of drug discovery.

Developing Robust Human Liver Microsomal Stability Prediction Models: Leveraging Inter-Species Correlation with Rat Data.

Objectives: Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of compounds in the early stages of drug discovery. At NCATS, a single-point rat liver microsomal (RLM) stability assay is used as a Tier I assay, while human liver microsomal (HLM) stability is employed as a Tier II assay. We experimentally screened and collected data on over 30,000 compounds for RLM stability and over 7000 compounds for HLM stability. Although HLM stability screening provides valuable insights, the increasing number of hits generated, along with the time- and resource-intensive nature of the assay, highlights the need for alternative strategies. One promising approach is leveraging in silico models trained on these experimental datasets. Methods: We describe the development of an HLM stability prediction model using our in-house HLM stability dataset. Results: Employing both classical machine learning methods and advanced techniques, such as neural networks, we achieved model accuracies exceeding 80%. Moreover, we validated our model using external test sets and found that our models are comparable to some of the best models in literature. Additionally, the strong correlation observed between our RLM and HLM data was further reinforced by the fact that our HLM model performance improved when using RLM stability predictions as an input descriptor. Conclusions: The best model along with a subset of our dataset (PubChem AID: 1963597) has been made publicly accessible on the ADME@NCATS website for the benefit of the greater drug discovery community. To the best of our knowledge, it is the largest open-source model of its kind and the first to leverage cross-species data.

The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform.

Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.