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:
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.
“The BioAscent in silico discovery team developed a QSAR model based on non-standard activity data that required adaptability and creativity from the BioAscent team. The QSAR model was then used to generate novel compounds and to enumerate focused arrays. Throughout the project, the BioAscent in silico team has been responsive and flexible, which helped us reach a successful conclusion.”
CTO, Tay Therapeutics
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
Discovery and characterization of novel TRPML1 agonists. X. Peng, C.J. Holler, A-M.F. Alves, M.G. Oliviera, M. Speake, A. Pugliese, M.R. Oskouei, I.D de Freitas, A.Y.-P. Chen, R. Gallegos, S.M. McTighe, G. Koenig, R.S. Hurst, J-F. Blain, J.C. Lanter, D.A. Burnett. View paper.
Implementation of an AI-assisted fragment-generator in an open-source platform. A. E. Bilsland, A. Pugliese, J. Bower. View paper.
Automated Generation of Novel Fragments Using Screening Data, a Dual SMILES Autoencoder, Transfer Learning and Syntax Correction. A. Bilsland, K. McAulay, R. West, A. Pugliese (corresponding author), J. Bower. View paper.
The identification and characterisation of autophagy inhibitors from the published kinase inhibitor sets. M. Zachari, J. Rainard, G. Pandarakalam, L. Robinson, J. Gillespie; M. Rajamanickam; V. Hamon; A. Morrison; I. Ganley; S. McElroy. View paper.
Synthesis and structure–activity relationships of N-(4-benzamidino)-oxazolidinones–potent and selective inhibitors of kallikrein-related peptidase 6; chemRxiv DOI: 10.26434/chemrxiv.9788276, 2019. E. De Vita, N. Smits, H. van den Hurk, E. Beck, J. Hewitt, G. Baillie, E. Russell, A. Pannifer, V. Hamon, A.Morrison, S. McElroy, P. Jones, N. Ignatenko, N. Gunkel and A. Miller. View paper.
Deep generative molecular design – AI at the service of the drug designer. Pharmacology Matters, British Pharmacological Society's online magazine, 2019. A. Pugliese and J. Bower. View paper.
Structure-based design, synthesis and biological evaluation of a novel series of isoquinolone and pyrazolo[4,3-c]pyridine inhibitors of fascin 1 as potential anti-metastatic agents. Bioorg Med Chem Lett. 2019; 29: 1023-9. S. Francis, et al. View paper.
Strategies for Fragment Library Design. In: Erlanson, D.A. and Jahnke W. eds. Fragment-based Drug Discovery: Lessons and Outlook. Wiley,pp. 101-117 2016. J. Bower, A. Pugliese, and M. Drysdale.
Identification of a selective G1-phase benzimidazolone inhibitor by a senescence-targeted virtual screen using artificial neural networks. Neoplasia. 2015 Sep; 17(9):704-15. A. Bilsland, A. Pugliese, et al. View paper.
Fragment-Based Hit Identification – Thinking in 3D. Drug Discov Today. 2013 Dec; 18 (23-24):1221-7. AD. Morley, A. Pugliese, et al. View paper.
Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med Chem. 2012 Oct; 4(15):1933-44. AV. Zakharov, ML. Peach, M. Sitzmann, IV. Filippov, HJ. McCartney, LH. Smith, A. Pugliese, MC Nicklaus. View paper.
Software and Resources for Computational Medicinal Chemistry. Future Med Chem. 2011 Jun; 3(8):1057-85. C. Liao, M. Sitzmann, A. Pugliese, M. Nicklaus. View paper.
Posters & case studies
The European Lead Factory: A Collaborative Approach to Drug Discovery. ESC Chemistry: F. MacLeod, A. Morrison, E. Beck, P. Jones, J. Hewitt, M. Matheson, J. Schulz, J. Robinson, L. Robinson, J. Gillespie, M. Huggett, M. Rajamanickam; ESC CMC: A. Pannifer, V. Hamon; ESC Biology: S. McElroy, M. Speake, G. Baillie, E. Russell, J. Rainard, A. Porter, G. Pandarakalam, N. Clark, D. Tegazzini. PPSC: H. Rutjes, C.A.A. van Boeckel, N. Smits, E. van Doornmalen, T. W. Lam, M. Bras, C. S. Schofield; J. J. Brem; M. A. McDonough, M. Van der Stelt, F. J. Janssen, P.P. Geurink, H. Ovaa, H. S. Overkleeft, A. C. M. van Esbroeck, M. P. Baggelaar, H. den Dulk. Download PDF
Harnessing the power of AI to focus compound collections: a collaboration with Medicines Discovery Catapult. Download PDF
