AIMC Topic: Drug Discovery

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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
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
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)
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
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
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
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 ...

GraphGIM: rethinking molecular graph contrastive learning via geometry image modeling.

BMC biology
BACKGROUND: Learning molecular representations is crucial for accurate drug discovery. Using graphs to represent molecules is a popular solution, and many researchers have used contrastive learning to improve the generalization of molecular graph rep...

BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention.

Scientific reports
The glucocorticoid receptor (GR) is a critical nuclear receptor that regulates a broad spectrum of physiological functions, including stress adaptation, immune response, and metabolism. Given the association between aberrant GR signaling and various ...

1,3-Thiazole nucleus as promising molecular platform against antimicrobial resistance: a recent overview in drug discovery.

European journal of medicinal chemistry
Antimicrobial resistance (AMR) has emerged as one of the foremost public health threats of the 21st century. The progressive decline in the efficacy of conventional antibiotics, combined with a scarcity of new drug classes approved in recent decades,...

Discovery of SARS-CoV-2 papain-like protease inhibitors through machine learning and molecular simulation approaches.

Drug discoveries & therapeutics
The papain-like protease (PLpro), a cysteine protease found in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), plays a crucial role in viral replication by cleaving the viral polyproteins and interfering with the host's innate immune re...