227 research outputs found
Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders
Chemical autoencoders are attractive models as they combine chemical space
navigation with possibilities for de-novo molecule generation in areas of
interest. This enables them to produce focused chemical libraries around a
single lead compound for employment early in a drug discovery project. Here it
is shown that the choice of chemical representation, such as SMILES strings,
has a large influence on the properties of the latent space. It is further
explored to what extent translating between different chemical representations
influences the latent space similarity to the SMILES strings or circular
fingerprints. By employing SMILES enumeration for either the encoder or
decoder, it is found that the decoder has the largest influence on the
properties of the latent space. Training a sequence to sequence heteroencoder
based on recurrent neural networks(RNNs) with long short-term memory cells
(LSTM) to predict different enumerated SMILES strings from the same canonical
SMILES string gives the largest similarity between latent space distance and
molecular similarity measured as circular fingerprints similarity. Using the
output from the bottleneck in QSAR modelling of five molecular datasets shows
that heteroencoder derived vectors markedly outperforms autoencoder derived
vectors as well as models built using ECFP4 fingerprints, underlining the
increased chemical relevance of the latent space. However, the use of
enumeration during training of the decoder leads to a markedly increase in the
rate of decoding to a different molecules than encoded, a tendency that can be
counteracted with more complex network architectures
APPLICAZIONE DI STRUMENTI DI CALCOLO AVANZATI PER LO STUDIO DI PROCESSI DI PIROLISI DI BIOMASSE
Sviluppo di un modello di pirolisi a parametri strutturali per biomasse (CPD)
Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain
Computer Assisted Synthesis Planning (CASP) has gained considerable interest as of late. Herein we investigate a template-based retrosynthetic planning tool, trained on a variety of datasets consisting of up to 17.5 million reactions. We demonstrate that models trained on datasets such as internal Electronic Laboratory Notebooks (ELN), and the publicly available United States Patent Office (USPTO) extracts, are sufficient for the prediction of full synthetic routes to compounds of interest in medicinal chemistry. As such we have assessed the models on 1731 compounds from 41 virtual libraries for which experimental results were known. Furthermore, we show that accuracy is a misleading metric for assessment of the policy network, and propose that the number of successfully applied templates, in conjunction with the overall ability to generate full synthetic routes be examined instead. To this end we found that the specificity of the templates comes at the cost of generalizability, and overall model performance. This is supplemented by a comparison of the underlying datasets and their corresponding models
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES
Using generative deep learning models and reinforcement learning together can
effectively generate new molecules with desired properties. By employing a
multi-objective scoring function, thousands of high-scoring molecules can be
generated, making this approach useful for drug discovery and material science.
However, the application of these methods can be hindered by computationally
expensive or time-consuming scoring procedures, particularly when a large
number of function calls are required as feedback in the reinforcement learning
optimization. Here, we propose the use of double-loop reinforcement learning
with simplified molecular line entry system (SMILES) augmentation to improve
the efficiency and speed of the optimization. By adding an inner loop that
augments the generated SMILES strings to non-canonical SMILES for use in
additional reinforcement learning rounds, we can both reuse the scoring
calculations on the molecular level, thereby speeding up the learning process,
as well as offer additional protection against mode collapse. We find that
employing between 5 and 10 augmentation repetitions is optimal for the scoring
functions tested and is further associated with an increased diversity in the
generated compounds, improved reproducibility of the sampling runs and the
generation of molecules of higher similarity to known ligands.Comment: 25 pages and 18 Figures. Supplementary material include
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