Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies
By Gabriele Macari, Daniele Toti, Fabio Polticelli. 2019
28 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Tommi S. Jaakkola
- Regina Barzilay
- Octavian-Eugen Ganea
- Hannes Stärk
- Lagnajit Pattanaik
- Wengong Jin
- José Miguel Hernández-Lobato
- Shitong Luo
In Molecular Biology
- Tommi S. Jaakkola
- Regina Barzilay
- Hannes Stärk
- Lagnajit Pattanaik
- Octavian-Eugen Ganea
- Gabriele Corso
- Bowen Jing
- Shuya Nakata
Citations in the corpus
- EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “Traditional computational methods for scanning proteins for their most ”druggable” areas have leveraged various views such as utilizing the protein’s 3D structure or/and residue sequence, extracting geometric features, building large template libraries, or relying on energy-based models (Macari et a”