SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.

Transforming growth factor β signaling overcomes dasatinib resistance in lung cancer.

Lung cancer is the second most common cancer and the leading cause of cancer-related deaths. Despite recent advances in the development of targeted therapies, patients with advanced disease remain incurable, mostly because metastatic non-small cell lung carcinomas (NSCLC) eventually become resistant to tyrosine kinase inhibitors (TKIs). Kinase inhibitors have the potential for target promiscuity because the kinase super family is the largest family of druggable genes that binds to a common substrate (ATP).

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org).

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design.

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists.

Web-based computational chemistry education with CHARMMing I: Lessons and tutorial.

This article describes the development, implementation, and use of web-based "lessons" to introduce students and other newcomers to computer simulations of biological macromolecules. These lessons, i.e.

Recent advances in proteasome inhibitor discovery.

Introduction: Proteasome inhibition is a quickly advancing subject of research and has a significant potential to become a potent therapeutic modality for many diseases and disorders. The aim of this review is to present the reader with the variety of approaches to the proteasome inhibitor discovery as well as highlight the diversity of scaffolds being considered for this task.

Areas Covered: This review focuses on current developments in proteasome inhibitor discovery, including an account of research efforts covered in the literature from the years 2009 - 2012, although some of the earlier work is also mentioned.

Virtual target screening: validation using kinase inhibitors.

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets.