Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …
Structure-based drug design with geometric deep learning
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
Can language models be used for real-world urban-delivery route optimization?
Language models have contributed to breakthroughs in interdisciplinary research, such as
protein design and molecular dynamics understanding. In this study, we reveal that beyond …
protein design and molecular dynamics understanding. In this study, we reveal that beyond …
Artificial intelligence for natural product drug discovery
Developments in computational omics technologies have provided new means to access
the hidden diversity of natural products, unearthing new potential for drug discovery. In …
the hidden diversity of natural products, unearthing new potential for drug discovery. In …
Foundational challenges in assuring alignment and safety of large language models
This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …
language models (LLMs). These challenges are organized into three different categories …
Prospective de novo drug design with deep interactome learning
De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …
chemical and pharmacological properties. We present a computational approach utilizing …
Pocketflow is a data-and-knowledge-driven structure-based molecular generative model
Y Jiang, G Zhang, J You, H Zhang, R Yao… - Nature Machine …, 2024 - nature.com
Deep learning-based molecular generation has extensive applications in many fields,
particularly drug discovery. However, the majority of current deep generative models are …
particularly drug discovery. However, the majority of current deep generative models are …
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 …
Machine learning in preclinical drug discovery
DB Catacutan, J Alexander, A Arnold… - Nature Chemical …, 2024 - nature.com
Drug-discovery and drug-development endeavors are laborious, costly and time consuming.
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …
These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of …
De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime
M Ballarotto, S Willems, T Stiller, F Nawa… - Journal of Medicinal …, 2023 - ACS Publications
Generative neural networks trained on SMILES can design innovative bioactive molecules
de novo. These so-called chemical language models (CLMs) have typically been trained on …
de novo. These so-called chemical language models (CLMs) have typically been trained on …