Site2vec: A Reference Frame Invariant Algorithm For Vector Embedding Of Protein-ligand Binding Sites

Abstract

Protein-ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm Site2Vec that derives reference frame invariant vector embe

Stats

• citations5
• S2 citations—
• github stars0
• HF likes0
• heat score5.84
• arxiv keybhadra2020site2vec

Related papers

• Learning Protein-ligand Binding In Hyperbolic Space (2025)0.00
• Learned Indexing In Proteins: Extended Work On Substituting Complex Distance Calculations With Embedding And Clustering Techniques (2022)5.84
• Distributed Representations For Biological Sequence Analysis (2016)0.00
• Aligning Proteins And Language: A Foundation Model For Protein Retrieval (2025)0.00
• Leanvec: Searching Vectors Faster By Making Them Fit (2023)0.00
• Fast And Scalable Gene Embedding Search: A Comparative Study Of FAISS And Scann (2025)2.26
• VERSE: Versatile Graph Embeddings From Similarity Measures (2018)17.42
• Prob2vec: Mathematical Semantic Embedding For Problem Retrieval In Adaptive Tutoring (2020)0.00