Artificial intelligence in drug discovery: recent advances and future perspectives
J Jiménez-Luna, F Grisoni, N Weskamp… - Expert opinion on drug …, 2021 - Taylor & Francis
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …
widespread adoption of machine learning, in particular deep learning, in multiple scientific …
Generative models for de novo drug design
Artificial intelligence (AI) is booming. Among various AI approaches, generative models
have received much attention in recent years. Inspired by these successes, researchers are …
have received much attention in recent years. Inspired by these successes, researchers are …
[HTML][HTML] Integrating structure-based approaches in generative molecular design
Generative molecular design for drug discovery and development has seen a recent
resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by …
resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by …
Deep Generative Models in De Novo Drug Molecule Generation
The discovery of new drugs has important implications for human health. Traditional
methods for drug discovery rely on experiments to optimize the structure of lead molecules …
methods for drug discovery rely on experiments to optimize the structure of lead molecules …
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
The field of machine learning for drug discovery is witnessing an explosion of novel
methods. These methods are often benchmarked on simple physicochemical properties …
methods. These methods are often benchmarked on simple physicochemical properties …
[HTML][HTML] Artificial intelligence for retrosynthesis prediction
In recent years, there has been a dramatic rise in interest in retrosynthesis prediction with
artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction …
artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction …
Geometric deep learning for structure-based ligand design
A pervasive challenge in drug design is determining how to expand a ligand─ a small
molecule that binds to a target biomolecule─ in order to improve various properties of the …
molecule that binds to a target biomolecule─ in order to improve various properties of the …
Exploring chemical space with score-based out-of-distribution generation
A well-known limitation of existing molecular generative models is that the generated
molecules highly resemble those in the training set. To generate truly novel molecules that …
molecules highly resemble those in the training set. To generate truly novel molecules that …
Hit and lead discovery with explorative rl and fragment-based molecule generation
Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties
has been highlighted as a promising strategy for drug design. Molecular docking program--a …
has been highlighted as a promising strategy for drug design. Molecular docking program--a …
Identification of novel discoidin domain receptor 1 (DDR1) inhibitors using E-pharmacophore modeling, structure-based virtual screening, molecular dynamics …
Dysregulation of the discoidin domain receptor (DDR1), a collagen-activated receptor
tyrosine kinase, has been linked to several human cancer diseases including non-small cell …
tyrosine kinase, has been linked to several human cancer diseases including non-small cell …