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381 Publications visible to you, out of a total of 381
Impact of the Journal of Chemical Information and Modeling Special Issue on Women in Computational Chemistry

Abstract

Not specified

Authors: Angelica Mazzolari, Ariane Nunes-Alves, Habibah A. Wahab, Rommie E. Amaro, Zoe Cournia, Kenneth M. Merz

Date Published: 27th Jul 2020

Publication Type: Journal

Myotubularin-related protein 7 activates peroxisome proliferator-activated receptor-gamma

Abstract

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Authors: Philip Weidner, Michaela Söhn, Torsten Schroeder, Laura Helm, Veronika Hauber, Tobias Gutting, Johannes Betge, Christoph Röcken, Florian N. Rohrbacher, Vijaya R. Pattabiraman, Jeffrey W. Bode, Rony Seger, Daniel Saar, Ariane Nunes-Alves, Rebecca C. Wade, Matthias P. A. Ebert, Elke Burgermeister

Date Published: 1st Jun 2020

Publication Type: Journal

Fine-Tuning of Sequence Specificity by Near Attack Conformations in Enzyme-Catalyzed Peptide Hydrolysis

Abstract

Not specified

Author: S. Kashif Sadiq

Date Published: 1st Jun 2020

Publication Type: Journal

Structural characterization of an Arf dimer interface: molecular mechanism of Arf‐dependent membrane scission

Abstract

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Authors: Petra Diestelkoetter‐Bachert, Rainer Beck, Inge Reckmann, Andrea Hellwig, Ana Garcia‐Saez, Monika Zelman‐Hopf, Anton Hanke, Ariane Nunes Alves, Rebecca C. Wade, Matthias P. Mayer, Felix Wieland

Date Published: 31st May 2020

Publication Type: Journal

Chromatosome Structure and Dynamics from Molecular Simulations

Abstract

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Authors: Mehmet Ali Öztürk, Madhura De, Vlad Cojocaru, Rebecca C. Wade

Date Published: 20th Apr 2020

Publication Type: Journal

Druggability Assessment in TRAPP Using Machine Learning Approaches

Abstract

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Authors: Jui-Hung Yuan, Sungho Bosco Han, Stefan Richter, Rebecca C. Wade, Daria B. Kokh

Date Published: 23rd Mar 2020

Publication Type: Journal

RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

Abstract (Expand)

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally … docking ligands into a protein binding site of interest, but this has the drawback of a large number of poses that must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast pre-filtering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance that is better than that of the original RASPD method and traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

Authors: Stefan Holderbach, Lukas Adam, B. Jayaram, Rebecca C. Wade, Goutam Mukherjee

Date Published: 2020

Publication Type: Journal

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