Search Results for author:
Found 7 papers, 4 papers with code
Vapur: A Search Engine to Find Related Protein - Compound Pairs in COVID-19 Literature
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
Structure-based drug discovery with deep learning
Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e. g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$.
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting.
DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models
Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models.
Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature
Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains.
Data and Representation for Turkish Natural Language Inference
In this paper, we offer a positive response for natural language inference (NLI) in Turkish.
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.
