![]() We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms.Ī molecular representation method that is continuous, data-driven, and can easily be converted into a machine-readable molecule has several advantages. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. ![]() ![]() The predictor estimates chemical properties from the latent continuous vector representation of the molecule. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. ![]() We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |