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Artificial intelligence (AI) drives innovation across society, economies and science. We argue for the importance of building AI technology according to open-source principles to foster accessibility, collaboration, responsibility and interoperability.
A pairwise binding comparison network (PBCNet) has been established for predicting the relative binding affinity among congeneric ligands, using a physics-informed graph attention mechanism with a pair of protein pocket-ligand complexes as input. PBCNet shows practical value in guiding structure-based drug lead optimization with speed, precision, and ease-of-use.
A physics-informed deep learning model, PBCNet, is proposed for predicting the relative binding affinity of ligands in order to improve guiding structure-based drug lead optimization.
Inspired by the classic lock-and-key model and advances in equivariant deep network design, we present a structure-based drug design model, SurfGen, which uses two types of equivariant graph neural networks to learn on protein surfaces and geometric structures to directly design small-molecule drugs.
We introduce STAligner — a graph neural network-based tool for the integration of multiple spatial transcriptomics datasets by generating batch effect-corrected embeddings, thereby enabling consensus spatial domain identification and accurate 3D tissue reconstruction.
Programmability is crucial in noisy intermediate-scale quantum computing, facilitating various functionalities for practical applications. An arbitrary programmable time-bin-encoded quantum boson sampling device has been developed, specifically tailored for potential drug discovery.
A graph attention neural network tool is introduced to integrate multiple spatial transcriptomics data from different individuals, technologies and developmental stages, enabling consensus spatial domain identification and three-dimensional tissue reconstruction.
This study develops a programmable quantum processor, named Abacus, and applies it to Gaussian boson sampling tasks targeting drug discovery challenges.
A guided diffusion model pushes the boundaries of de novo molecular design, extensively exploring the chemical space and generating chemical compounds that satisfy custom target criteria.
Dr Barbara Liskov — a mostly retired Institute Professor at the Massachusetts Institute of Technology, a pioneer in object-oriented programming and distributed systems and the winner of the 2008 ACM A. M. Turing Award, which is the highest distinction in computer science — talks to Nature Computational Science about her work on data abstractions, her career trajectory and recognizing the contributions of women in computer science.
Ada Lovelace Day celebrates women in STEM careers, but also raises awareness of the challenges that women have faced in science, as well as the importance of female role models in STEM.
Dr Angela K. Wilson, director of the Michigan State University Center for Quantum Computing, Science and Engineering and John A. Hannah Distinguished Professor at Michigan State University, talks to Nature Computational Science about protein-based carbon-capture, the use of machine learning in computational chemistry, and making the research field more equitable for female researchers.
Dr Diyi Yang, Assistant Professor of computer science at Stanford University, talks to Nature Computational Science about understanding human communication in a social context, building natural language processing systems that are human-centered, and the challenges that female researchers face in the field.
SurfGen is a structure-based drug design approach that delves into topological and geometric deep learning techniques for interaction learning, echoing the classical lock-and-key model.
The reasoning capabilities of OpenAI’s generative pre-trained transformer family were tested using semantic illusions and cognitive reflection tests that are typically used in human studies. While early models were prone to human-like cognitive errors, ChatGPT decisively outperformed humans, avoiding the cognitive traps embedded in the tasks.
GaUDI is a guided diffusion method for the design of molecular structures that features a flexible and scalable target function and that achieves high validity of generated molecules.
