KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
I Cortés-Ciriano, A Bender - Journal of cheminformatics, 2019 - Springer
The application of convolutional neural networks (ConvNets) to harness high-content
screening images or 2D compound representations is gaining increasing attention in drug …
screening images or 2D compound representations is gaining increasing attention in drug …
DrugGPT: A GPT-based strategy for designing potential ligands targeting specific proteins
DrugGPT presents a ligand design strategy based on the autoregressive model, GPT,
focusing on chemical space exploration and the discovery of ligands for specific proteins …
focusing on chemical space exploration and the discovery of ligands for specific proteins …
Guiding conventional protein–ligand docking software with convolutional neural networks
The high-performance computational techniques have brought significant benefits for drug
discovery efforts in recent decades. One of the most challenging problems in drug discovery …
discovery efforts in recent decades. One of the most challenging problems in drug discovery …
A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine Learning Derived Bio-computation
K Uppalapati, E Dandamudi, SN Ice, G Chandra… - arXiv preprint arXiv …, 2024 - arxiv.org
Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial
Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here …
Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here …
Big data in drug discovery
Drug discovery is a challenging and complicated process that requires decades to discover
and develop a drug. This process can be streamlined and simplified by using big data. Big …
and develop a drug. This process can be streamlined and simplified by using big data. Big …
Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions
E Moman, MA Grishina, VA Potemkin - Journal of Computer-Aided …, 2019 - Springer
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern
drug discovery and one that still poses major challenges. The choice of the appropriate …
drug discovery and one that still poses major challenges. The choice of the appropriate …
[PDF][PDF] Molecular modelling and machine learning for the investigation of 2-oxazolidinone ribosomal antibacterials
MK Buckley - 2023 - eprints.qut.edu.au
This thesis by publication compares computational methods to analyze an untested dataset
of oxazolidinones, a type of antibacterials that target ribosomes. The study evaluates …
of oxazolidinones, a type of antibacterials that target ribosomes. The study evaluates …
Conservation of red blood cell signalling in Plasmodium merozoite invasion
JJM Yong - 2023 - dr.ntu.edu.sg
Red blood cells (RBC) possess essential signalling cascades that are involved in
Plasmodium merozoite invasion. While P. falciparum reticulocyte binding protein homologue …
Plasmodium merozoite invasion. While P. falciparum reticulocyte binding protein homologue …
and Vigneshwaran Namasivayam
S Bhattarai, R Kumar, S Nag - Machine Learning and Systems …, 2022 - books.google.com
Drug discovery is a challenging and complicated process that requires decades to discover
and develop a drug. This process can be streamlined and simplified by using big data. Big …
and develop a drug. This process can be streamlined and simplified by using big data. Big …