Computational chemistry at Janssen

@article{Vlijmen2017ComputationalCA,  title={Computational chemistry at Janssen},  author={Herman van Vlijmen and Renee L. DesJarlais and Taraneh Mirzadegan},  journal={Journal of Computer-Aided Molecular Design},  year={2017},  volume={31},  pages={267-273},  url={https://api.semanticscholar.org/CorpusID:207166545}}
An overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the Computational Chemistry group on Drug Discovery at Janssen are given.

16 Citations

Contemporary Computational Applications and Tools in Drug Discovery.

Many computational tools, platforms, and applications that are currently available are catalogued, with four main areas highlighted: commercially available tools/platforms, open-source applications, internally developed platforms (software tools developed within a pharma or biotech organization), and artificial intelligence/machine learning-based platforms.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

An overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results is presented, with a focus on real-world drug discovery applications.

Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

A novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented, with a significant speed up of over tenfold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed.

The in silico drug discovery toolbox: applications in lead discovery and optimization.

The early steps of drug-discovery pipeline are analysed, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

It is shown that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.

Pharmacophore modeling, atom-based 3D-QSAR and molecular docking studies on N-benzylpyrimidin-4-amine derivatives as VCP/p97 inhibitors

Molecular docking studies indicated that the H-bond and hydrophobic interactions existed between the inhibitors and p97, which was consistent with the results of 3D-QSAR, which provided some useful information for designing new and effective p97 inhibitors.

Protein-Ligand Binding Free Energy Calculations with FEP.

This chapter outlines the methodological advances in FEP+, including the OPLS3 force fields, the REST2 enhanced sampling, the incorporation of REST2 sampling with conventional FEP (Free Energy Perturbation) through FEP/REST, and the advanced simulation setup and data analysis.

Acceleration of the GROMACS Free-Energy Perturbation Calculations on GPUs

These advancements aim to provide the alchemical free-energy community with a fast and efficient way of conducting FEP calculations, thereby paving the way for a highly accurate and computationally efficient solution in predicting ligand–protein binding free energies.

Deep Learning-based Ligand Design using Shared Latent Implicit Fingerprints from Collaborative Filtering

This work uses deep learning techniques to translate latent descriptors into discrete representations of molecules (SMILES), without explicitly optimizing for chemical properties, which allows the design of new compounds based upon the latent representation of nearby proteins, thereby encoding drug-like properties including binding affinities to known proteins.

Implicit-descriptor ligand-based virtual screening by means of collaborative filtering

This work proposes and evaluates methods that do not require explicit feature vectorization through fingerprinting, but, instead, provide implicit descriptors based only on other known assays, and shows that implicit methods significantly outperform traditional machine learning methods.

31 References

Advanced Biological and Chemical Discovery (ABCD): Centralizing Discovery Knowledge in an Inherently Decentralized World

ABCD is an attempt to bridge multiple continents, data systems, and cultures using modern information technology and to provide scientists with tools that allow them to analyze multifactorial SAR and make informed, data-driven decisions.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

An approach to designing tight-binding ligands with a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches is reported, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Computational Fragment-Based Approach at PDB Scale by Protein Local Similarity

A database of MED-Portions is built, where a MED-Portion is a new structural object encoding protein-fragment binding sites, and this protocol is applied to two targets that represent important protein superfamilies in drug design: a protein kinase and a G-Protein Coupled Receptor.

The ChEMBL bioactivity database: an update

More comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications and a new richer data model for representing drug targets has been developed.

LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters

Three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compound databases.

Development and validation of a genetic algorithm for flexible docking.

GOLD (Genetic Optimisation for Ligand Docking) is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding.

DrugBank: a comprehensive resource for in silico drug discovery and exploration

DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug data with comprehensive drug target information and is fully searchable supporting extensive text, sequence, chemical structure and relational query searches.

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