Computational chemistry, CADD (Computer-Aided Drug Design), cheminformatics and data analysis applications are a key component of any science-led drug discovery project. These approaches have been demonstrated to reduce R&D costs and time and add valuable scientific insights. They are applied at all stages of the drug discovery process to focus effort, impact projects and rapidly move compounds towards the clinic. Outsourcing computational chemistry can play an important role in maximising your R&D outcome.
BioAscent’s computational drug designers work with you to support your drug discovery projects, suggesting and applying the right computational methodologies to help solve the challenges inevitably encountered during the discovery process.
“BioAscent computational chemists worked on a structure-based drug design project where there was a Cryo-EM structure of the target. Working on Cryo-EM models is challenging and BioAscent generated valid docking hypothesis and design concepts which resulted in a series of compounds which showed good activity and enabled us to establish new IP position. The BioAscent team is collaborative and professional. Their extensive experience in structure-based drug design allowed them to make a significant impact on the progress of the project. We would happily recommend the BioAscent In Silico Discovery team for computational projects.”
Director of Medicinal Chemistry, US Biotech
Our In Silico Discovery and Data Analysis scientists have years of knowledge and success applying multiple ligand-based and structure-based computational methodologies at all stages of the drug discovery workflow. Our capabilities include:
Structure and ligand-based ligandability evaluation
Sequence alignment, refining protein models derived from electron density data, binding site identification, homology modelling, loop modelling, protein-protein docking, in silico mutagenesis analysis/protein stability
Standardisation, ligands alignment and conformational search, strain energy evaluation, MM-based torsion scan
Fragments and small-molecules library curation and design
Virtual fragments and small-molecules screening, HTS triage, design of hit expansion sets
Rigid/induced-fit docking, covalent docking, pharmacophore modelling and search, interaction fingerprints
Fragment analogues search, combinatorial fragment expansion, fragment growing and linking
3D QSAR/QSPR modelling, scaffold hopping, bioisostere search, ADMETox and physicochemical properties prediction, solvent analysis, multi-parameter optimisation
Clustering, diversity analysis, similarity and substructure search, filtering databases based on substructure matching and property values, self-organising map
Supervised and unsupervised learning, data visualisation/storytelling
Drug discovery is a cross-functional process. Our computational chemists work in concert with you and our medicinal chemistry and biosciences groups, navigating and avoiding the common pitfalls associated with the discovery process, and taking your project from concept to candidate in the most timely and efficient way.
BioAscent’s computing capabilities include a rack mount Xeon-based Dell server to support error checking and memory correction, leading to improved stability and less data corruption. This configuration can be easily upgraded and extended to accommodate increased computing needs for specific client projects. In addition to multi-core Xeon CPUs, GPU acceleration is guaranteed by an array of the latest NVidia Ampere architecture graphics cards.
Our server is accessible via individual workstations, these workstations also being available for data analysis and less computationally intensive tasks.
Our hardware together with a state-of-the-art molecular modelling platform allows us, for example, to run ultra-large virtual screenings in the order of billions of compounds in a ligand-based fashion and millions of compounds in a structure-based fashion.
We use innovative commercial and open-source modelling programs, pipelining tools and databases. Depending on the project’s needs, they can be combined and extended to maximise their effectiveness.
“We have been working with the In Silico Discovery team at BioAscent for over a year.
The first target we collaborated on was very challenging because the activity data were unusual. BioAscent proposed a machine learning approach to model that non-linear activity and predict the activity of a set of new compounds.
We then began collaborating on a second target. BioAscent performed MD simulations, docking studies, focused-library design, structure-based and ligand-based virtual screening. Homology modelling was also carried out for series of proteins to understand the potential selectivity issues among isoforms.
Overall, the BioAscent computational chemistry team has been innovative, reliable, and collaborative. Their work has resulted in meaningful scientific insights and idea generation and made a significant impact on the direction and progress of several of our small molecule drug discovery projects. ”
Director Medicinal Chemistry, Global Pharmaceutical Company
Posters & case studies