De Novo Drug Design with Deep Generative Models : an Empirical Study
نویسنده
چکیده
We present an empirical study about the usage of RNN generative models for stochastic optimization in the context of de novo drug design. We study different kinds of architectures and we find models that can generate molecules with higher values than ones seen in the training set. Our results suggest that we can improve traditional stochastic optimizers, that rely on random perturbations or random sampling by using generative models trained on unlabeled data, to perform knowledge-driven optimization.
منابع مشابه
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has largely focused on generating SMILES strings instead of molecular graphs. Although current graph generative models are available, they are often too general and computationally expensive, which restricts their application to molecules with small sizes. I...
متن کاملDeep Reinforcement Learning for De-Novo Drug Design
We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates two deep neural networks – generative and predictive – that are trained separately but employed jointly to generate novel chemical structures with the desired properties. Generative models are trained to produce chemicall...
متن کاملGenerative Recurrent Networks for De Novo Drug Design
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This ...
متن کاملFr\'echet ChemblNet Distance: A metric for generative models for molecules
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, assessing the performance of such generative models is notoriously difficult. Metrics that are typically used to assess the performance of such generative models are the percentage of chemically valid molecules or the similarity to real molecules in t...
متن کاملGenerating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate ...
متن کامل