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Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of
compound-protein interactions for rational drug discovery. Existing deep learning-based …
compound-protein interactions for rational drug discovery. Existing deep learning-based …
Current computational tools for protein lysine acylation site prediction
Z Qin, H Ren, P Zhao, K Wang, H Liu… - Briefings in …, 2024 - academic.oup.com
As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs)
play crucial roles in regulating diverse functions of proteins. With recent advancements in …
play crucial roles in regulating diverse functions of proteins. With recent advancements in …
[HTML][HTML] Applications of artificial intelligence to lipid nanoparticle delivery
Lipid nanoparticles (LNPs) are nanocarriers composed of four lipid components and can be
used for gene therapy, protein replacement, and vaccine development. However, LNPs also …
used for gene therapy, protein replacement, and vaccine development. However, LNPs also …
Ppflow: Target-aware peptide design with torsional flow matching
Therapeutic peptides have proven to have great pharmaceutical value and potential in
recent decades. However, methods of AI-assisted peptide drug discovery are not fully …
recent decades. However, methods of AI-assisted peptide drug discovery are not fully …
Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach
Explainable and interpretable unsupervised machine learning helps one to understand the
underlying structure of data. We introduce an ensemble analysis of machine learning …
underlying structure of data. We introduce an ensemble analysis of machine learning …
Recent advances in interpretable machine learning using structure-based protein representations
Recent advancements in machine learning (ML) are transforming the field of structural
biology. For example, AlphaFold, a groundbreaking neural network for protein structure …
biology. For example, AlphaFold, a groundbreaking neural network for protein structure …
MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization
We present a scalable, end-to-end workflow for protein design. By augmenting protein
sequences with natural language descriptions of their biochemical properties, we train …
sequences with natural language descriptions of their biochemical properties, we train …
Quantifying the hardness of bioactivity prediction tasks for transfer learning
Today, machine learning methods are widely employed in drug discovery. However, the
chronic lack of data continues to hamper their further development, validation, and …
chronic lack of data continues to hamper their further development, validation, and …