New SILCS-MC Study Demonstrates Improved Performance with Machine Learning Optimization

  New SILCS-MC Study Demonstrates Improved Performance with Machine Learning Optimization    May 16, 2019, Baltimore, MD – SilcsBio, a company that develops commercial software and related services for structure-based drug design, today announced that established Site-identification by ligand competitive saturation-Monte Carlo (SILCS-MC) approaches were applied to seven protein targets and 551 ligands, correctly predicting[…]

SilcsBio, LLC Announces Release of CGenFF 2.3.0

Baltimore, MD, 26 April, 2019 In ongoing efforts to improve the coverage of the CGenFF program, version 2.3.0 of the CGenFF program improves support for a variety of molecules via explicit parametrization of the molecules outlined below and features improvements for halogen-protein interactions. Molecules explicitly parametrized The functional groups in these molecules were previously accessible[…]

SilcsBio, LLC Receives NIH Funding to Expand Offerings in Biologics

FOR IMMEDIATE RELEASE: April 11, 2019 Ken Malone SilcsBio, LLC 410.929.2305 Ken.Malone@silcsbio.com   SilcsBio, LLC Receives NIH Funding to Expand Offerings in Biologics   Baltimore, Maryland: SilcsBio, LLC announced today a $225,000 award from the National Institutes of Health to expand on its broadly used software, SILCS, to enable its use with biological therapeutics, especially for[…]

SilcsBio, LLC Announces Issuance of US Patent with Utility in Drug Design Software

FOR IMMEDIATE RELEASE: July 12, 2018 Ken Malone SilcsBio, LLC 410.929.2305 Ken.Malone@silcsbio.com SilcsBio, LLC Announces Issuance of US Patent with Utility in Drug Design Software July 12, 2018 Baltimore, Maryland: SilcsBio, LLC (SilcsBio) announced today the issuance of a US patent covering key elements of SilcsBio’s drug design software. The SilcsBio software is broadly used[…]

SILCS-MC ranks top 10 on Free Energy Set 1 in the D3R Grand Challenge 2 on FXR

Grand Challenge 2 from D3R provided a farnesoid X receptor (FXR) target dataset to participants, which was used to test the performance of various computational approaches. SilcsBio’s SILCS-MC approach ranked 4th in Stage 1 and 6th in Stage 2 on the Free Energy Set 1 of this challenge. We used our unique SILCS approach to[…]