With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they entirely overlook the genetic profile and properties of the target disease. In the case of cancer, this is problematic since it is a highly genetic disease in which the biomolecular profile of target cells determines the response to therapy. Here, we introduce the first deep generative model capable of generating anticancer compounds given a target biomolecular profile. Using a reinforcement learning framework, the transcriptomic profile of cancer cells is used as a context in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile . Our molecule generator combines two  pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile. The efficacy predictor is used to optimize the generated molecules through a reward determined by the predicted IC50 drug sensitivity for the generated molecule and the target profile . We demonstrate how the molecule generation can be biased towards compounds with high  inhibitory effect against individual cell lines or specific cancer sites. We verify our approach by investigating candidate drugs generated against specific cancer types and investigate their structural similarity to existing compounds with known efficacy against these cancer types. We envision our approach to transform in silico anticancer drug design by increasing success rates in lead compound discovery via leveraging the biomolecular characteristics of the disease  .
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the genetic profile and properties of the target disease. Here, we introduce the first generative model capable of tailoring anticancer compounds for a specific biomolecular profile. Using a RL framework, the transcriptomic profiles of cancer cells are used as a context for the generation of candidate molecules . Our molecule generator combines two separately pretrained variational autoencoders (VAEs) - the first VAE encodes transcriptomic profiles into a smooth, latent space which in turn is used to condition a second VAE to generate novel molecular structures on the given transcriptomic profile. The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i.e., transcriptomic profile) . We demonstrate how the molecule generation can be biased towards compounds with high predicted inhibitory effect against individual cell lines or specific cancer sites. We verify our approach by investigating candidate drugs generated against specific cancer types and find the highest structural similarity to existing compounds with known efficacy against these cancer types. We envision our approach to transform in silico anticancer drug design by leveraging the biomolecular characteristics of the disease in order to increase success rates in lead compound discovery .
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entirely overlook
disregard
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In the case of cancer, this is problematic since it is a highly genetic disease in which the biomolecular profile of target cells determines the response to therapy.
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deep
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generating anticancer compounds given a target
tailoring anticancer compounds for a specific
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reinforcement learning
RL
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profile
profiles
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is
are
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in which anticancer molecules are generated and optimized to obtain effective compounds for the given profile
for the generation of candidate molecules
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separately
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and a multimodal efficacy predictor
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generates transcriptomic profiles while the second conditional VAE generates
encodes transcriptomic profiles into a smooth, latent space which in turn is used to condition a second VAE to generate
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conditioned
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efficacy predictor is used to optimize the generated molecules through a reward determined by the predicted IC50 drug sensitivity for the generated molecule and the target profile
generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i.e., transcriptomic profile)
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predicted
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investigate their
find the highest
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increasing success rates in lead compound discovery via
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in order to increase success rates in lead compound discovery