Our platform technology comprises multiple software technologies. These can be used independently, but also work synchronously within the SILCS core platform, to create end-to-end drug design workflow.



Site Identification by Ligand Competitive Saturation (SILCS) generates 3D maps (FragMaps) of interaction patterns for chemical functional groups with your target molecule.

Innovative Ligand Design & Optimization with SILCS

Site Identification by Ligand Competitive Saturation (SILCS) generates 3D maps (FragMaps) of interaction patterns for chemical functional groups with your target molecule.

SILCS reveals intricacies of dynamics and provides tools to optimize ligand scaffolds using qualitative and quantitative binding pockets insights, enabling more rapid and effective drug design.

SILCS – Enhanced Mixed Solvent MD

SILCS uses multiple small molecule probes with various functional groups, explicit solvent modeling, and target molecule flexibility to perform protein target mapping.

A Grand Canonical Monte Carlo approach drives probe sampling beyond the limits of diffusion and makes SILCS particularly useful for probing cryptic/transient binding pockets.

                                                                 FragMap of p38 MAP kinase-ligand complex colored by fragment type: green = apolar, red = H-bond acceptor, blue = H-bond donor & cyan = positive charge. FragMap densities near the bound inhibitor provide options of ligand optimization.

Let FragMaps Guide Your Lead Discovery

Conveniently explore FragMaps in our user interface or in your favorite molecular visualization software, including Autodock Tools, Pymol MOE, and VMD.

Visualize favorable interactions with the target macromolecule.

Gain insights to design better ligands with optimally placed functional groups.

User Interface

New point-and-click platform for simulation system preparation, job submission to remote host, job progress tracking, and results visualization.

In this GPCR, SILCS correctly predicts functional group affinities of a known ligand while simultaneously offering optimization solutions for this ligand. This approach has identified novel agonists of the β2-Adrenergic Receptor.

Insightful & Versatile

SILCS enables discovery of deep binding pockets in proteins and enzymes, including membrane bound proteins.

Easy to Use

A command-line tool automates setup for transmembrane proteins (GPCRs and more).

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The CHARMM General Force Field (CGENFF) program generates comprehensive parameters and topology information for a wide range of drug-like molecules.

Rapid Automation

CGenFF enables use of a wide range of diverse organic molecules in computer-aided drug design efforts.

Computational chemists can obtain parameter and topology files for their organic molecules in less than 0.01 second with this rapid, automated tool.

These comprehensive parameter files contain information on atom connectivities, atom charges, atom types, bond angles, dihedral angles, bond force constants, and the empirical force field parameters required for a range of molecular modeling methods, molecular dynamics simulation packages and related technologies.

Easy to Use

Input a molecular structure in MOL2 format, then CGenFF does the rest!

Wide Coverage

CGenFF can be used for >90% of drug-like molecules.


Parameter output format is compatible with CHARMM force fields & a range of modeling & simulation packages.


Iteratively & rapidly process large number of organic compounds.


Parameter generation in less than 0.01 second per compound on a single core node

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Single-Step Free Energy Perturbation (SSFEP) delivers free energy calculations 1,000 times faster than with standard FEP. Evaluate thousands of ligand modifications per hour without compromising predictability.

Rapid calculations by Single-Step Free Energy Perturbation (SSFEP) minimize lead optimization times in drug discovery.

Free Energy Perturbation (FEP) is the standard approach to calculate relative free energies, but it is often impractical, due to its large computational burden. SSFEP provides an alternative that is 1,000 times faster than standard FEP, while maintaining comparable accuracy in predicting whether modified ligands bind the target protein better or worse than the parent ligand. After a conditioning step involving the target and ligand, SSFEP uses an automated ligand scanning approach to calculate relative free energies for around 1,000 ligand transformations per hour.

A graphical user interface facilitates selection of parent ligand R-sites to be modified and provides a library of modifications.

SSFEP for Diverse Protein Targets

Single-Step FEP methodology can be employed during lead optimization in any structure-based drug discovery project. It has been validated across diverse gene family targets including, but not limited to, traditional targets like kinases, proteases, nuclear hormone receptors, and novel epigenetic targets, like methyl transferases and bromodomains.

Best For

Small functional group changes, like -H to -Cl, -OH, -CH3, etc.

Quick Set-Up

Target conditioning step requires only 30 minutes of set-up time and 3 hours of computer time (on 12 cores + 1 GPU node).

Evaluating a range of modifications at various ligand R-sites reveals which R-sites
are best suited to modification. This information provides focus in downstream
ligand optimization efforts.

Fast Results

SSFEP evaluates around 1,000 ligand modifications per hour.

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SILCS-Biologics software builds on the patented SILCS technology, using protein-protein interaction and excipient binding predictions to give insights on excipient formulation for biological therapeutics.

New developments enable a leading-edge approach to analyze protein-excipient/buffer and protein-protein interactions at an atomic level and give insight on excipient formulation for biologic therapeutics, including antibodies and other proteins.

The Problem

The formulation of biologics has become a major bottleneck in bringing these novel therapies to market. Biologics are formulated to maintain their activity during long-term storage and subsequent administration. Maintaining their 3D conformations and preventing protein aggregation is an important challenge in their development. Experiments to identify the optimal combinations of excipients, buffers, and surfactants are low-throughput and require high concentrations of protein – which is limiting and costly. Failure to determine the optimal formulation results in decreased product performance, impacting patient treatment and in some cases requiring the costly reengineering of the therapeutic protein.

FragMaps on CNTO607 mAb
variable domain

SilcsBio Innovation

SilcsBio is developing a novel formulation design technology in silico to inform the selection of desired excipient/buffer combinations. With the help of the established SILCS platform, SilcsBio’stechnology 3D maps the protein of interest (FragMap) and its functional group affinity pattern, providing data to enhance stability and prevent aggregation.


Screening of hundreds of excipients and buffers, as well as protein-protein interaction (PPI), within a day, minimizing the need for experimental screening.

Rational selection of excipient/buffer combinations in silico, guided by SilcsBio’s approach, lowering costs and truncating timelines in developing novel biologics.

Improved formulations to better maintain the stability of biologics during storage and delivery.

PPI and Excipient Binding Data Are Combined to Score Various Excipients

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Connect with us today to learn more about how we can help your team work faster and more efficiently.

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