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DeepPurpose, a PyTorch‑based AI toolkit developed by Harvard researchers, provides COVID‑19 bioassay data and 56 cutting‑edge models that enable rapid drug‑target affinity prediction, virtual screening, and drug repurposing with just a few lines of code, dramatically shortening new‑drug development cycles.
This article explains how to use the Kubernetes-native Argo Workflows engine to automate and scale complex gene-computing pipelines, detailing its advantages, challenges, and a step-by-step BWA alignment workflow example on Alibaba Cloud’s ACK platform.
HelixFold‑Multimer, a new Baidu PaddleHelix model, outperforms AlphaFold 3 on antigen‑antibody and peptide‑protein complex predictions, achieving mean DockQ scores of 0.41 and 0.38 respectively and success rates up to 77 % when epitope data are used, and is already deployed in large‑molecule drug pipelines.
This article reviews the fundamentals of graph neural networks and graph representation learning, explains why proteins can be modeled as graphs, and surveys recent GNN‑based applications such as structure prediction, function annotation, protein design, and self‑supervised representation learning, concluding with future research directions.
The article introduces the MEGA-Protein tool, which integrates an AI‑driven MSA engine and the MindSpore framework to overcome AlphaFold 2’s limitations and achieve high‑accuracy protein structure prediction, and also announces the MSG Enterprise Tour and Hangzhou Developer Day event on April 27.
Virus phylogeny explores how genetic mutations, recombination, and evolutionary rates shape viral lineages, using tree-based methods such as distance, maximum parsimony, maximum likelihood, and Bayesian approaches, while addressing challenges like variable molecular clocks, recombination, and limited sampling to infer relationships and origins.
This presentation by Stanford PhD student Huang Kexin explores the challenges and innovations of applying graph machine learning to molecular and biomedical networks, introducing specialized GNN architectures, actionable hypothesis generation, domain‑scientist interfaces, few‑shot learning, and the Therapeutics Data Commons for accelerating drug discovery.
This article presents the Adaptive Universal Generalized PageRank Graph Neural Network (GPR‑GNN), explains the two main limitations of existing GNNs—lack of generality across homophilic and heterophilic graphs and the over‑smoothing problem—and demonstrates through synthetic and real‑world experiments that GPR‑GNN achieves robust node classification while remaining interpretable and parameter‑efficient.
Yang Zhentao’s 2018 conference talk surveys data‑visualization fundamentals, highlights the multidisciplinary skills required, introduces the open‑source Circos tool and its polar‑coordinate workflow, showcases genomic and business use cases, and compares alternative platforms, emphasizing data quality, query capability, and proper view selection.