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 …
Accurate structure prediction of biomolecular interactions with AlphaFold 3
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of
proteins and their interactions, enabling a huge range of applications in protein modelling …
proteins and their interactions, enabling a huge range of applications in protein modelling …
ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling
Most molecular generative models based on artificial intelligence for de novo drug design
are ligand-centric and do not consider the detailed three-dimensional geometries of protein …
are ligand-centric and do not consider the detailed three-dimensional geometries of protein …
Diffusion models in protein structure and docking
Generative AI is rapidly transforming the frontier of research in computational structural
biology. Indeed, recent successes have substantially advanced protein design and drug …
biology. Indeed, recent successes have substantially advanced protein design and drug …
[HTML][HTML] 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 …
Symmetry-informed geometric representation for molecules, proteins, and crystalline materials
Artificial intelligence for scientific discovery has recently generated significant interest within
the machine learning and scientific communities, particularly in the domains of chemistry …
the machine learning and scientific communities, particularly in the domains of chemistry …
[HTML][HTML] Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning
Late-stage functionalization is an economical approach to optimize the properties of drug
candidates. However, the chemical complexity of drug molecules often makes late-stage …
candidates. However, the chemical complexity of drug molecules often makes late-stage …
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
Deep generative models for structure-based drug design (SBDD), where molecule
generation is conditioned on a 3D protein pocket, have received considerable interest in …
generation is conditioned on a 3D protein pocket, have received considerable interest in …
[HTML][HTML] 3D molecular generative framework for interaction-guided drug design
Deep generative modeling has a strong potential to accelerate drug design. However,
existing generative models often face challenges in generalization due to limited data …
existing generative models often face challenges in generalization due to limited data …