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Found 6 papers, 2 papers with code
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge.
WideDTA: prediction of drug-target binding affinity
In addition, the results showed that, given the protein sequence and ligand SMILES, the inclusion of protein domain and motif information as well as ligand maximum common substructure words do not provide additional useful information for the deep learning model.
ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction
Our aim is to process the patterns in SMILES as a language to predict protein-ligand affinity, even when we cannot infer the function from the sequence.
DeepDTA: Deep Drug-Target Binding Affinity Prediction
The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction.
Ranked #1 on
Drug Discovery
on BindingDB IC50
A novel methodology on distributed representations of proteins using their interacting ligands
We showed that ligand-based protein representation, which uses only SMILES strings of the ligands that proteins bind to, performs as well as protein-sequence based representation methods in protein clustering.
BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain
A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods.
Ranked #8 on
Sentence Embeddings For Biomedical Texts
on BIOSSES
(using extra training data)
