Computationally guided high-throughput design of self-assembling drug nanoparticles
D. Reker, Y. Rybakova, A. R. Kirtane, R. Cao, J. W. Yang, N. Navamajiti, A. Gardner, R. M. Zhang, T. Esfandiary, J. L'Heureux, T. von Erlach, E. M. Smekalova, D. Leboeuf, K. Hess, A. Lopes, , G. Traverso
This paper develops a high-throughput screening platform with machine learning to design self-assembling drug nanoparticles, identifying 101 novel formulations from 2.1 million combinations.
bioRxiv · Sep 30, 2019Bioengineering