Advances of artificial intelligence in anti-cancer drug design: a review of the past decade
L Wang, Y Song, H Wang, X Zhang, M Wang, J He… - Pharmaceuticals, 2023 - mdpi.com
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-
consuming, and challenging task. How to reduce the research costs and speed up the …
consuming, and challenging task. How to reduce the research costs and speed up the …
Sequence-based drug design as a concept in computational drug design
L Chen, Z Fan, J Chang, R Yang, H Hou, H Guo… - Nature …, 2023 - nature.com
Drug development based on target proteins has been a successful approach in recent
decades. However, the conventional structure-based drug design (SBDD) pipeline is a …
decades. However, the conventional structure-based drug design (SBDD) pipeline is a …
The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges
Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug
discovery has come to the forefront. It reduces the time and expenditure. Due to these …
discovery has come to the forefront. It reduces the time and expenditure. Due to these …
Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign)
Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …
Recent advances have shown great potential in applying machine learning (ML) for PLA …
Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
Drug–drug interactions (DDIs) can trigger unexpected pharmacological effects on the body,
and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been …
and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been …
Drug–target binding affinity prediction model based on multi-scale diffusion and interactive learning
Z Zhu, X Zheng, G Qi, Y Gong, Y Li, N Mazur… - Expert Systems with …, 2024 - Elsevier
Drug–target interactions (DTIs) play a key role in drug discovery and development as they
are critical in understanding the complex mechanisms of underlying drugs and their …
are critical in understanding the complex mechanisms of underlying drugs and their …
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug–Target interaction prediction
Drug–target interaction (DTI) prediction reduces the cost and time of drug development, and
plays a vital role in drug discovery. However, most of research does not fully explore the …
plays a vital role in drug discovery. However, most of research does not fully explore the …
Meta learning with graph attention networks for low-data drug discovery
Finding candidate molecules with favorable pharmacological activity, low toxicity, and
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …
AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and
design. Traditional experiments are very expensive and time-consuming. Recently, deep …
design. Traditional experiments are very expensive and time-consuming. Recently, deep …