PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning
| Autor: | Ali Oskooei, Joris Cadow, Matteo Manica, Greta Markert, Jannis Born, María Rodríguez Martínez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Drug Computer science Structural similarity Systems biology media_common.quotation_subject Science 02 engineering and technology Computational biology Transcriptome 03 medical and health sciences Molecule Reinforcement learning Transcriptomics media_common Multidisciplinary Systems Biology 021001 nanoscience & nanotechnology Autoencoder 3. Good health 030104 developmental biology Complex System Biology Computer Science 0210 nano-technology Function (biology) |
| Zdroj: | iScience, Vol 24, Iss 4, Pp 102269-(2021) iScience, 24 (4) |
| ISSN: | 2589-0042 |
| Popis: | With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the cellular environment of target diseases. Bridging systems biology and drug design, we present a reinforcement learning method for de novo molecular design from gene expression profiles. We construct a hybrid Variational Autoencoder that tailors molecules to target-specific transcriptomic profiles, using an anticancer drug sensitivity prediction model (PaccMann) as reward function. Without incorporating information about anticancer drugs, the molecule generation is biased toward compounds with high predicted efficacy against cell lines or cancer types. The generation can be further refined by subsidiary constraints such as toxicity. Our cancer-type-specific candidate drugs are similar to cancer drugs in drug-likeness, synthesizability, and solubility and frequently exhibit the highest structural similarity to compounds with known efficacy against these cancer types. ISSN:2589-0042 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|