On the shortcomings of continuous representations of chemical space



Original Source Here

Norachem’s generative design is uniquely suited to answer the challenges raised by (Zhavoronkov et al., 2020) and the subsequent commentary. We postpone a detailed comparative analysis between Norachem and GENTRL to a future article.

Conclusions

The main conclusion is that gradient-based methods are a sub-optimal way of searching the immense and unstructured drug-like chemical space. Any attempt to project drug-like chemical space down to a continuous representation will result in a loss of information and lead to inferior drug candidates.

Norachem’s generative design overcomes these limitations by not restricting itself to continuous representations, and by using its artificial intelligence to optimise heuristics for searching different areas of chemical space.

References

Gomez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernandez-Lobato, J. M., Sanchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., Aspuru-Guzik, A. (2018). Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 4 (2), 268–276

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., Shayakhmetov, R. R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., \[Ellipsis] Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology 37, 1038–1040

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