Computational approaches have revolutionized the field of drug discovery, collectively
known as Computer-Assisted Drug Design (CADD). Advancements in computing power …

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern
drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively …

In order to explore the role of topological indices for predicting physio-chemical properties of
anti-HIV drugs, this research uses python program-based algorithms to compute topological …

Identifying the frequencies of drug-side effects is crucial for assessing drug risk-benefit.
However, accurately determining these frequencies remains challenging due to the …

Predicting the pharmacological activity, toxicity, and pharmacokinetic properties of
molecules is a central task in drug discovery. Existing machine learning methods are …

Manual annotation of ultrasound images relies on expert knowledge and requires significant
time and financial resources. Semi-supervised learning (SSL) exploits large amounts of …

The remarkable success of Graph Neural Networks underscores their formidable capacity to
assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of …

The great learning ability of deep learning facilitates us to comprehend the real physical
world, making learning to simulate complicated particle systems a promising endeavour …

A drug molecule is a substance that changes an organism's mental or physical state. Every
approved drug has an indication, which refers to the therapeutic use of that drug for treating …

Multi-drug combination therapies are increasingly used for complex diseases but carry risks
of harmful drug interactions. Effective drug–drug interaction prediction (DDIP) is essential for …