AIMC Topic: Drug Discovery
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ML enhanced bioactivity prediction for angiotensin II receptor: A potential anti-hypertensive drug target.
Scientific reports
Jul 14, 2025
The process of drug discovery is intricate, and encompasses a series of detailed phases of research, development, and testing, aimed at evaluating the safety and effectiveness of prospective therapeutic agents. Artificial Intelligence has emerged as ...
Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence.
Nature communications
Jul 12, 2025
The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discov...
Machine-Learning-Driven Discovery of -Phenylbenzenesulfonamides as a Novel Chemotype for Lactate Dehydrogenase A Inhibition with Anti-Pancreatic Cancer Activity.
Journal of medicinal chemistry
Jul 11, 2025
Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in aerobic glycolysis. Despite extensive efforts, the structural diversity of LDHA inhibitors remains limited. Here, we utilized machine learning techniqu...
Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.
Journal of chemical information and modeling
Jul 9, 2025
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges ...
Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.
Cell communication and signaling : CCS
Jul 8, 2025
Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within breast cancer classifications, marked by rapid progression and high mortality. Abnormal activation o...
Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.
Journal of computer-aided molecular design
Jul 8, 2025
Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors tha...
Unlocking the Potential: The Structural Wonders and Diverse Applications of Triazoles in Contemporary Science.
Topics in current chemistry (Cham)
Jul 4, 2025
Triazoles, a captivating class of nitrogen-containing heterocyclic compounds, have emerged as pivotal players in contemporary chemistry, drawing significant attention for their exceptional versatility and wide-ranging applications. They have become e...
DCGCN: Dual-Channel Graph Convolutional Network-Based Drug-Target Interaction Prediction Method with 3D Molecular Structure.
Journal of chemical information and modeling
Jul 2, 2025
Exploring drug-target interactions (DTIs) is crucial for drug discovery. Most existing methods for predicting DTIs rely solely on the linear structures of molecules, such as SMILES or the amino acid sequence. However, these linear features fail to re...
EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions.
Journal of chemical information and modeling
Jul 2, 2025
Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representations, they critically overlook th...
Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.
Scientific reports
Jul 2, 2025
In drug discovery, different data modalities (chemical structure, cell biology, quantum mechanics, etc.) are abundant, and their integration can help with understanding aspects of chemistry, biology, and their interactions. Within cell biology, cell ...
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