Ross P. Hryczanek
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ross P. Hryczanek has a total of 52 co-authors. Here are the top co-authors by number of shared publications:
Alex Phillipou
GSK, Medicines Research Centre
2 shared publications- Christopher P. Tinworth
GSK, Gunnels Wood Road, Stevenage SG1 2NY, U.K.
2 shared publications - Efstratios Stratikos
National Centre for Scientific Research Demokritos Agia Paraskevi Greece
2 shared publications Hester Sheehan
GSK, Medicines Research Centre
2 shared publicationsJonathan P. Hutchinson
GSK, Medicines Research Centre
2 shared publicationsMargarete Neu
GSK, Medicines Research Centre
2 shared publications
Latest Publications
- 13 April 2026
- Published by American Chemical Society (ACS) in Journal of Medicinal Chemistry
- Vol. 69 (8) , 8869-8896
Automated Molecular Design in BRADSHAW, Applied to the Optimization of ERAP1 Inhibitors
Generative design and machine learning are increasingly prevalent in medicinal chemistry. To pilot the comprehensive use of automated molecular design on a project, the BRADSHAW platform was used to optimize a series of inhibitors of Endoplasmic Reticulum Aminopeptidase 1 (ERAP1), an emerging target in cancer immunotherapy and autoimmune diseases. Through four consecutive iterations applying in silico molecular generation, property prediction and filtering, we conducted a multiparameter optimization of potency, physicochemical properties and pharmacokinetics. Continuous refinement of Machine Learning (ML) models led to improved scoring accuracy and compound quality, culminating in identification of in vitro and in vivo tool molecules. We also discuss our reflections on the pilot and integration of automated design into medicinal chemistry projects, including observations of the human factors resulting from increased use of computational design, and recommendations for future projects.
- 13 April 2026
- Published by American Chemical Society (ACS) in Journal of Medicinal Chemistry
- Vol. 69 (8) , 8869-8896
Automated Molecular Design in BRADSHAW, Applied to the Optimization of ERAP1 Inhibitors
Generative design and machine learning are increasingly prevalent in medicinal chemistry. To pilot the comprehensive use of automated molecular design on a project, the BRADSHAW platform was used to optimize a series of inhibitors of Endoplasmic Reticulum Aminopeptidase 1 (ERAP1), an emerging target in cancer immunotherapy and autoimmune diseases. Through four consecutive iterations applying in silico molecular generation, property prediction and filtering, we conducted a multiparameter optimization of potency, physicochemical properties and pharmacokinetics. Continuous refinement of Machine Learning (ML) models led to improved scoring accuracy and compound quality, culminating in identification of in vitro and in vivo tool molecules. We also discuss our reflections on the pilot and integration of automated design into medicinal chemistry projects, including observations of the human factors resulting from increased use of computational design, and recommendations for future projects.
- Open Access
- 6 November 2024
- Published by American Chemical Society (ACS) in ACS Medicinal Chemistry Letters
- Vol. 15 (12) , 2107-2114
Optimization of Potent and Selective Cyclohexyl Acid ERAP1 Inhibitors Using Structure- and Property-Based Drug Design
Endoplasmic reticulum aminopeptidase 1 (ERAP1) cleaves the N-terminal amino acids of peptides, which can then bind onto major histocompatibility class I (MHC-I) molecules for presentation onto the cell surface, driving the activation of adaptive immune responses. In cancer, overtrimming of mature antigenic peptides can reduce cytotoxic T-cell responses, and ERAP1 can generate self-antigenic peptides which contribute to autoimmune cellular responses. Therefore, modulation of ERAP1 activity has potential therapeutic indications for cancer immunotherapy and in autoimmune disease. Herein we describe the hit-to-lead optimization of a series of cyclohexyl acid ERAP1 inhibitors, found by X-ray crystallography to bind at an allosteric regulatory site. Structure-based drug design enabled a >1,000-fold increase in ERAP1 enzymatic and cellular activity, resulting in potent and selective tool molecules. For lead compound 7, rat pharmacokinetic properties showed moderate unbound clearance and oral bioavailability, thus highlighting the promise of the series for further optimization.
- 6 November 2024
- Published by American Chemical Society (ACS) in ACS Medicinal Chemistry Letters
- Vol. 15 (12) , 2107-2114
Open AccessOptimization of Potent and Selective Cyclohexyl Acid ERAP1 Inhibitors Using Structure- and Property-Based Drug Design
Endoplasmic reticulum aminopeptidase 1 (ERAP1) cleaves the N-terminal amino acids of peptides, which can then bind onto major histocompatibility class I (MHC-I) molecules for presentation onto the cell surface, driving the activation of adaptive immune responses. In cancer, overtrimming of mature antigenic peptides can reduce cytotoxic T-cell responses, and ERAP1 can generate self-antigenic peptides which contribute to autoimmune cellular responses. Therefore, modulation of ERAP1 activity has potential therapeutic indications for cancer immunotherapy and in autoimmune disease. Herein we describe the hit-to-lead optimization of a series of cyclohexyl acid ERAP1 inhibitors, found by X-ray crystallography to bind at an allosteric regulatory site. Structure-based drug design enabled a >1,000-fold increase in ERAP1 enzymatic and cellular activity, resulting in potent and selective tool molecules. For lead compound 7, rat pharmacokinetic properties showed moderate unbound clearance and oral bioavailability, thus highlighting the promise of the series for further optimization.