Stefano Forli

Orcid: 0000-0002-5964-7111

According to our database1, Stefano Forli authored at least 25 papers between 2007 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species.
J. Chem. Inf. Model., September, 2023

Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results.
J. Chem. Inf. Model., April, 2023

2022
Benchmarking the performance of irregular computations in AutoDock-GPU molecular docking.
Parallel Comput., 2022

Structure-based virtual screening workflow to identify antivirals targeting HIV-1 capsid.
J. Comput. Aided Mol. Des., 2022

2021
Improving Docking Power for Short Peptides Using Random Forest.
J. Chem. Inf. Model., 2021

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.
J. Chem. Inf. Model., 2021

2020
Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.
J. Chem. Inf. Model., 2020

Parallelizing Irregular Computations for Molecular Docking.
Proceedings of the 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, 2020

Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking.
Proceedings of the 28th Euromicro International Conference on Parallel, 2020

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research.
Proceedings of the BCB '20: 11th ACM International Conference on Bioinformatics, 2020

2019
Integrative modeling of the HIV-1 ribonucleoprotein complex.
PLoS Comput. Biol., 2019

Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network.
J. Chem. Inf. Model., 2019

AutoGridFR: Improvements on AutoDock Affinity Maps and Associated Software Tools.
J. Comput. Chem., 2019

D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.
J. Comput. Aided Mol. Des., 2019

Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4.
J. Comput. Aided Mol. Des., 2019

AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions.
Bioinform., 2019

2016
Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.
J. Chem. Inf. Model., 2016

2015
<i>AutoDockFR</i>: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.
PLoS Comput. Biol., 2015

A Virtual Screen Discovers Novel, Fragment-Sized Inhibitors of <i>Mycobacterium tuberculosis</i> InhA.
J. Chem. Inf. Model., 2015

2014
AutoDock4<sub>Zn</sub>: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins.
J. Chem. Inf. Model., 2014

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge.
J. Comput. Aided Mol. Des., 2014

Blind prediction of HIV integrase binding from the SAMPL4 challenge.
J. Comput. Aided Mol. Des., 2014

Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge.
J. Comput. Aided Mol. Des., 2014

2012
Cyclin-dependent kinases 5 template: Useful for virtual screening.
Comput. Biol. Medicine, 2012

2007
Lennard-Jones Potential and Dummy Atom Settings to Overcome the AUTODOCK Limitation in Treating Flexible Ring Systems.
J. Chem. Inf. Model., 2007


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