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Found 6 papers, 1 papers with code
HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights
While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field.
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models
In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance.
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative
Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information.
TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response
Our study highlights the prediction power of TCR and its potential value for cancer drug repurpose and precision oncology treatment.
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer
Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.
Study on MCS Selection and Spectrum Allocation for URLLC Traffic under Delay and Reliability Constraint in 5G Network
Theoretical results in the scenario of a 5G New Radio system are presented, where the SNR thresholds for adaptive modulation and coding scheme selection, transmission rate and delay, as well as admission region under different configurations are discussed.
