Multiple machine learning methods aided virtual screening of NaV 1.5 inhibitors

Dynamic Changes in Ion Channels during Myocardial Infarction and Therapeutic Challenges

In different areas of the heart, action potential waveforms differ due to differences in the expressions of sodium, calcium, and potassium channels. One of the characteristics of myocardial infarction (MI) is an imbalance in oxygen supply and demand, leading to ion imbalance. After MI, the regulation and expression levels of K+, Ca2+, and Na+ ion channels in cardiomyocytes are altered, which affects the regularity of cardiac rhythm and leads to myocardial injury. Myocardial fibroblasts are the main effector cells in the process of MI repair. The ion channels of myocardial fibroblasts play an important role in the process of MI. At the same time, a large number of ion channels are expressed in immune cells, which play an important role by regulating the in- and outflow of ions to complete intracellular signal transduction. Ion channels are widely distributed in a variety of cells and are attractive targets for drug development. This article reviews the changes in different ion channels after MI and the therapeutic drugs for these channels. We analyze the complex molecular mechanisms behind myocardial ion channel regulation and the challenges in ion channel drug therapy.

Machine learning study of the extended drug-target interaction network informed by pain related voltage-gated sodium channels

ABSTRACT

Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein-protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug-target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)-based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.

Identification of (4-chlorophenyl)(5-hydroxynaphtho[1,2-b]furan-3-yl)methanone as novel COX-2 inhibitor with analgesic profile

Chronic pain is a serious problem that affects billions of people worldwide, but current analgesic drugs limit their use in chronic pain management due to their respective side effects. As a first-line clinical drug for chronic pain, COX-2 selective inhibitors can relieve mild to moderate pain, but they also have some problems. The most prominent one is that their analgesic intensity is not enough, and they cannot well meet the treatment needs of chronic pain. Therefore, there is an urgent need to develop COX-2 inhibitors with stronger analgesic intensity. In this article, we used virtual screening method to screen out the structurally novel COX-2 inhibitor for chronic pain management, and conducted a preliminary study on its mechanism of action using molecular dynamics simulation.

Employing Molecular Conformations for Ligand-Based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning

Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed with attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.