Generative machine learning for de novo drug discovery: A systematic review
DD Martinelli - Computers in Biology and Medicine, 2022 - Elsevier
Recent research on artificial intelligence indicates that machine learning algorithms can
auto-generate novel drug-like molecules. Generative models have revolutionized de novo …
auto-generate novel drug-like molecules. Generative models have revolutionized de novo …
Generative deep learning for targeted compound design
In the past few years, de novo molecular design has increasingly been using generative
models from the emergent field of Deep Learning, proposing novel compounds that are …
models from the emergent field of Deep Learning, proposing novel compounds that are …
Sample efficiency matters: a benchmark for practical molecular optimization
Molecular optimization is a fundamental goal in the chemical sciences and is of central
interest to drug and material design. In recent years, significant progress has been made in …
interest to drug and material design. In recent years, significant progress has been made in …
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …
innovatiaon and impact. However, advancement in this field requires formulation of …
[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 …
Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
Inverse design allows the generation of molecules with desirable physical quantities using
property optimization. Deep generative models have recently been applied to tackle inverse …
property optimization. Deep generative models have recently been applied to tackle inverse …
Reinforced genetic algorithm for structure-based drug design
Abstract Structure-based drug design (SBDD) aims to discover drug candidates by finding
molecules (ligands) that bind tightly to a disease-related protein (targets), which is the …
molecules (ligands) that bind tightly to a disease-related protein (targets), which is the …
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 …
Defining and exploring chemical spaces
CW Coley - Trends in Chemistry, 2021 - cell.com
Designing functional molecules with desirable properties is often a challenging, multi-
objective optimization. For decades, there have been computational approaches to facilitate …
objective optimization. For decades, there have been computational approaches to facilitate …
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 …