Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br /

De novo design of protein structure and function with RFdiffusion.

Acknowledgements: We thank N. Anand and D. Tischer for helpful discussions, and I. Kalvet and Y. Kipnis for providing helpful Rosetta scripts. We thank A. Dosey for the provision of purified influenza HA protein. We thank R. Wu, J. Mou, K. Choi, L. Wu and D. Blei for valuable feedback during writing. We thank I. Haydon for help with graphics. We also thank L. Goldschmidt and K. VanWormer, respectively, for maintaining the computational and wet laboratory resources at the Institute for Protein Design. This work was supported by gifts from Microsoft (D.J., M.B. and D.B.), Amgen (J.L.W.), the Audacious Project at the Institute for Protein Design (B.L.T., I.S., J.Y., H.E. and D.B.), the Washington State General Operating Fund supporting the Institute for Protein Design (P.V. and I.S.), grant no. INV-010680 from the Bill and Melinda Gates Foundation (W.B.A., D.J., J.W. and D.B.), grant no. DE-SC0018940 MOD03 from the US Department of Energy Office of Science (A.J.B. and D.B.), grant no. 5U19AG065156-02 from the National Institute for Aging (S.V.T. and D.B.), an EMBO long-term fellowship no. ALTF 139-2018 (B.I.M.W.), the Open Philanthropy Project Improving Protein Design Fund (R.J.R. and D.B.), The Donald and Jo Anne Petersen Endowment for Accelerating Advancements in Alzheimer’s Disease Research (N.R.B.), a Washington Research Foundation Fellowship (S.J.P.), a Human Frontier Science Program Cross Disciplinary Fellowship (grant no. LT000395/2020-C, L.F.M.), an EMBO Non-Stipendiary Fellowship (grant no. ALTF 1047-2019, L.F.M.), the Defense Threat Reduction Agency grant nos. HDTRA1-19-1-0003 (N.H. and D.B.) and HDTRA12210012 (F.D.), the Institute for Protein Design Breakthrough Fund (A.C. and D.B.), an EMBO Postdoctoral Fellowship (grant no. ALTF 292-2022, J.L.W.) and the Howard Hughes Medical Institute (A.C., W.S., R.J.R. and D.B.), an NSF-GRFP (J.Y.), an NSF Expeditions grant (no. 1918839, J.Y., R.B. and T.S.J.), the Machine Learning for Pharmaceutical Discovery and Synthesis consortium (J.Y., R.B. and T.S.J.), the Abdul Latif Jameel Clinic for Machine Learning in Health (J.Y., R.B. and T.S.J.), the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program (J.Y., R.B. and T.S.J.), EPSRC Prosperity Partnership grant no. EP/T005386/1 (E.M.) and the DARPA Accelerated Molecular Discovery program and the Sanofi Computational Antibody Design grant (J.Y., R.B. and T.S.J.). We thank Microsoft and AWS for generous gifts of cloud computing resources.There has been considerable recent progress in designing new proteins using deep-learning methods1-9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications