NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries
<|reference_start|>NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries: We present NEBULA, the first latent 3D generative model for scalable generation of large molecular libraries around a seed compound of interest. Such libraries are crucial for scientific discovery, but it remains challenging to generate large numbers of high quality samples efficiently. 3D-voxel-based methods have recently shown great promise for generating high quality samples de novo from random noise (Pinheiro et al., 2023). However, sampling in 3D-voxel space is computationally expensive and use in library generation is prohibitively slow. Here, we instead perform neural empirical Bayes sampling (Saremi & Hyvarinen, 2019) in the learned latent space of a vector-quantized variational autoencoder. NEBULA generates large molecular libraries nearly an order of magnitude faster than existing methods without sacrificing sample quality. Moreover, NEBULA generalizes better to unseen drug-like molecules, as demonstrated on two public datasets and multiple recently released drugs. We expect the approach herein to be highly enabling for machine learning-based drug discovery. The code is available at https://github.com/prescient-design/nebula<|reference_end|>
@article{nowara2024nebula:,
    title={NEBULA: Neural Empirical Bayes Under Latent Representations for
  Efficient and Controllable Design of Molecular Libraries},
    author={Ewa M. Nowara, Pedro O. Pinheiro, Sai Pooja Mahajan, Omar Mahmood,
  Andrew Martin Watkins, Saeed Saremi, Michael Maser},
    journal={arXiv preprint arXiv:2407.03428},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03428},
    primaryClass={cs.LG q-bio.BM}
}