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 …
In silico chemical experiments in the Age of AI: From quantum chemistry to machine learning and back
A Aldossary, JA Campos‐Gonzalez‐Angulo… - Advanced …, 2024 - Wiley Online Library
Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …
predicting chemical properties. However, traditional computational methods face significant …
DrugCLIP: Contrasive Protein-Molecule Representation Learning for Virtual Screening
Virtual screening, which identifies potential drugs from vast compound databases to bind
with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional …
with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional …
BigBind: learning from nonstructural data for structure-based virtual screening
Deep learning methods that predict protein–ligand binding have recently been used for
structure-based virtual screening. Many such models have been trained using protein …
structure-based virtual screening. Many such models have been trained using protein …
A review on graph neural networks for predicting synergistic drug combinations
M Besharatifard, F Vafaee - Artificial Intelligence Review, 2024 - Springer
Combinational therapies with synergistic effects provide a powerful treatment strategy for
tackling complex diseases, particularly malignancies. Discovering these synergistic …
tackling complex diseases, particularly malignancies. Discovering these synergistic …
Neural multi-task learning in drug design
Multi-task learning (MTL) is a machine learning paradigm that aims to enhance the
generalization of predictive models by leveraging shared information across multiple tasks …
generalization of predictive models by leveraging shared information across multiple tasks …
Knowledge-augmented graph machine learning for drug discovery: A survey from precision to interpretability
The integration of Artificial Intelligence (AI) into the field of drug discovery has been a
growing area of interdisciplinary scientific research. However, conventional AI models are …
growing area of interdisciplinary scientific research. However, conventional AI models are …
Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
T Harren, T Gutermuth, C Grebner… - Wiley …, 2024 - Wiley Online Library
Abstract Structure‐based drug design is a widely applied approach in the discovery of new
lead compounds for known therapeutic targets. In most structure‐based drug design …
lead compounds for known therapeutic targets. In most structure‐based drug design …
Maximally expressive GNNs for outerplanar graphs
We propose a linear time graph transformation that enables the Weisfeiler-Leman (WL) test
and message passing graph neural networks (MPNNs) to be maximally expressive on …
and message passing graph neural networks (MPNNs) to be maximally expressive on …
Hitvisc: high-throughput virtual screening as a service
N Nikitina, E Ivashko - International Conference on Parallel Computing …, 2023 - Springer
High-performance and high-throughput computing play an important role in drug
development and, in particular, in solving the computationally intensive problem of virtual …
development and, in particular, in solving the computationally intensive problem of virtual …