Deep learning techniques have been hugely successful for traditional supervised and unsupervised machine learning problems. In large part, these techniques solve continuous optimization problems. Recently however, discrete generative deep learning models have been successfully used to efficiently search high-dimensional discrete spaces. These methods work by representing discrete objects as sequences, for which powerful sequence-based deep models can be employed. Unfortunately, these techniques are significantly hindered by the fact that these generative models often produce invalid sequences. As a step towards solving this problem, we propose to learn a deep recurrent validator model. Given a partial sequence, our model learns the probability of that sequence occurring as the beginning of a full valid sequence. Thus this identifies valid versus invalid sequences and crucially it also provides insight about how individual sequence elements influence the validity of discrete objects. To learn this model we propose an approach inspired by seminal work in Bayesian active learning. On a synthetic dataset, we demonstrate the ability of our model to distinguish valid and invalid sequences. We believe this is a key step toward learning generative models that faithfully produce valid discrete objects.
Current methods for attacking these discrete search problems work by lifting' the search from discrete space to continuous space, via an @cite_0 . Specifically, an autoencoder jointly learns two mappings: 1) a mapping from discrete space to continuous space called an ; and 2) a reverse mapping from continuous space back to discrete space called a . These mappings are learned so that if we map a discrete object to a continuous one via the encoder, then map it back via the decoder, we reconstruct the original object. The hope is that, once the autoencoder is fully trained, the continuous space (often called the latent' space) acts as proxy for the discrete space. If this holds, we can use the geometry of the continuous space to improve search using (Euclidean) distance measures and gradients, among many other things. showed that is possible to use this technique to search for promising drug molecules.
@cite_0
2529996553
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. 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. 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. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. 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. Continuous represent...