Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Drug development is a complex and time-consuming endeavor that traditionally relies on the
experience of drug developers and trial-and-error experimentation. The advent of artificial …

Accurate and rapid prediction of protein–ligand interactions (PLIs) is the fundamental
challenge of drug discovery. Deep learning methods have been harnessed for this purpose …

Hitherto virtual screening (VS) has been typically performed using a structure-based drug
design paradigm. Such methods typically require the use of molecular docking on high …

Abstract Machine learning is rapidly advancing the drug discovery process, significantly
enhancing speed and efficiency. Innovation in computer-aided drug design is primarily …

Tumor necrosis factor-α-induced protein 8-like 3 (TNFAIP8L3 or TIPE3) functions as a
transfer protein for lipid second messengers. TIPE3 is highly upregulated in several human …

Drug design has always been pursuing techniques with time-and cost-benefits. Virtual
screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) …

Improving the generalization ability of scoring functions remains a major challenge in protein–
ligand binding affinity prediction. Many machine learning methods are limited by their …

As ultra-large-scale virtual screening becomes critical for early-stage drug discovery, highly
efficient screening methods are gaining prominence. Deep-learning-based approaches …

Optimizing molecules to improve their properties is a fundamental challenge in drug design.
For a fine-tuning of molecular properties without losing bio-activity validated in advance, the …