A new publication describes and update to AutoDock Vina "AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings" DOI.

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs.

The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

• AutoDock4.2 and Vina scoring functions
• Support of simultaneous docking of multiple ligands and batch mode for virtual screening
• Support of macrocycle molecules
• Hydrated docking protocol
• Can write and load external AutoDock maps
• Python bindings for Python 3 (Linux and Mac)
• AutoDock Vina is distributed under the Apache License, Version 2.0.

 

The amount of chemical space that is now accessible has increased rapidly over the last couple of years with vendors now offering billions of molecules either available from stock or via rapid synthesis, this has made searching increasingly difficult. infiniSee is a tool for searching these vast chemical spaces rapidly using a feature tree search.

Under the hood lies the FTrees similarity engine. FTrees employs a fuzzy pharmacophore descriptor that is able to pick out structurally distinct (therefore distant) molecules that are actually close neighbours in pharmacophore space.

This major update introduces a new user interface making infiniSee more intuitive and easy-to-use. In addition to the new interface, the update brings a new mode called Web Service. It allows power-users to shift the heavy-duty computing to a different machine.

There is a review of an older version of infiniSee here.

 

infiniSee from BioSoveIT enables similarity searching, based on a query molecule, through billions of compounds in chemical spaces. Currently two chemical spaces from commercial compound vendors can be explored: the well-known Enamine REAL Space with 13 billion compounds and WuXi’s GalaXi with 1,7 billion compounds.

 

A very interesting paper on Virtual Chemical Libraries by W. Patrick Walters DOI describing how it is now possible to generate virtual libraries of molecules of billions of compounds. These vast virtual libraries result in a number of practical challenges in particular their use in virtual screening.

If we consider a virtual screen with a false positive rate of 1% (an optimistic estimate for even the best virtual screening methods), a virtual screen on a library of 1 million molecules would yield 10,000 false positive hits. (A “false positive” is an inactive molecule which is predicted to be active).

Another consideration with very large virtual libraries is the time and CPU resource required for processing, whilst substructure and 2D similarity searches are very fast and can make use of hashed fingerprints. 3D or docking searches are orders of magnitude slower and require either storage of multiple conformations of the ligand or conformation generation on the fly. Realistically these require access to large compute clusters, cloud based resources are now relatively accessible but require significant expertise to access efficiently and securely.

Even the fastest docking programs require 2 seconds per molecule to dock an ensemble of conformations into a protein binding site. At this rate, approximately 15,327 CPU days would be required to dock 680 million molecules.

With this in mind it perhaps appropriate to flag that D3R Grand Challenge 4 has just opened, Full details are published on the Drug Design Data Resource site.

 

A recent publication "Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control" {DOI](http://dx.doi.org/10.1007/s10822-018-0100-7) describes a very interesting software tool for virtual screening.

While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional (3D) ligand. Two factors contribute: the general difficulty of discovering potent inhibitors, and the lack of free, user-friendly software to incorporate project-specific knowledge and user hypotheses into 3D ligand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind.

The Screenlamp homepage gives more details and installation instructions. Screenlamp is written in Python (3.6) and can be downloaded from GitHub https://github.com/psa-lab/screenlamp.

Certain submodules within screenlamp require external software to sample low-energy conformations of molecules and to generate pair-wise overlays. The tools that are currently being used in the pre-built, automated screening pipeline are OpenEye OMEGA and OpenEye ROCS to accomplish those tasks. However, screenlamp does not strictly require OMEGA and ROCS, and you are free to use any open source alternative that provided that the output files are compatible with screenlamp tools, which uses the MOL2 file format.

Screenlamp is research software and has been made available to other researchers under a permissive Apache v2 open source license.

 

OpenEye have announced the release of OpenEye Toolkits v2015.October. These libraries include the usual support for C++, Python, C# and Java.

New Features

• FastROCS TK was added to the OpenEye toolkits collection
• Molecule reading performance improvement in OEChem TK
• The capabilities of the OEBio-Fragment Network have been expanded
• 213 new ring templates have been added to the OEChem TK built-in ring dictionary

The full release notes give more details

In particular note the 2015.Oct release is the last to support Mac OSX 10.8 so time to upgrade if you have not already done so.

 

Grand Challenge 2015: Prediction of ligand poses, and affinity rankings, for the protein targets HSP90 and MAP4K4. Stage 1 predictions are due November 16, 2015; Stage 2 predictions are due February 1, 2016. For details, and to sign up and participate, please see https://drugdesigndata.org/about/grand-challenge-2015

SAMPL5: Prediction of aqueous host-guest binding free energies and, optionally, enthalpies for three host-guest series. A series of aqueous-organic partition coefficients may also be added in the next several weeks. Predictions are due February 1, 2016. For details, and to participate, please see https://drugdesigndata.org/about/sampl5.

These challenges are organized by the Drug Design Data Resource, which is based at UC San Diego and supported by a grant (U01GM111528) from the NIH's National Institute of General Medical Sciences. They are made possible by generous donations of data, pre-publication, from AbbVie, Genentech, the CSAR initiative at U. Michigan, and Professors Lyle Isaacs (U. Maryland) and Bruce Gibb (Tulane U.)

 

OpenEye have just announced the release of a new version of ROCS.

ROCS is a fast shape comparison application, based on the idea that molecules have similar shape if their volumes overlay well and any volume mismatch is a measure of dissimilarity. It uses a smooth Gaussian function to represent the molecular volume, so it is possible to routinely minimize to the best global match. Novel and interesting molecular scaffolds can be identified using ROCS against targets often considered very difficult for computational techniques to address.