A5068060471- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Analytical Chemistry and Chromatography
- Protein Structure and Dynamics
- Crystallography and molecular interactions
- Viral Infectious Diseases and Gene Expression in Insects
- Microbial Natural Products and Biosynthesis
- BIM and Construction Integration
- Reinforcement Learning in Robotics
- Innovative Microfluidic and Catalytic Techniques Innovation
- Receptor Mechanisms and Signaling
Paris Biotech Santé
2022
Centre National de la Recherche Scientifique
2019
Université de Strasbourg
2019
Barcelona Biomedical Research Park
2019
Universitat Pompeu Fabra
2019
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 libraries around a single lead compound employment early drug discovery project. Here, it is shown that the choice representation, such strings from simplified molecular-input line-entry system (SMILES), has large influence on properties latent space. It further explored what extent translating...
Here we show that Generative Topographic Mapping (GTM) can be used to explore the latent space of SMILES-based autoencoders and generate focused molecular libraries interest. We have built a sequence-to-sequence neural network with Bidirectional Long Short-Term Memory layers trained it on SMILES strings from ChEMBL23. Very high reconstruction rates test set molecules were achieved (>98%), which are comparable ones reported in related publications. Using GTM, visualized autoencoder...
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire discover effective bioactive molecules. To address this shortcoming, we propose a generative adversarial network generate, rather than search, diverse three-dimensional ligand shapes complementary pocket. Furthermore, show that generated molecule can decoded using shape-captioning into sequence of SMILES enabling directly de novo drug design....
We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (M
Over the last decade, there has been significant progress in field of machine learning for de novo drug design, particularly deep generative models. However, current approaches exhibit a challenge as they do not ensure that proposed molecular structures can be feasibly synthesized nor provide synthesis routes small molecules, thereby seriously limiting their practical applicability. In this work, we propose novel forward framework powered by reinforcement (RL) Policy Gradient Forward...
Reinforcement learning (RL) has made significant progress in both abstract and real-world domains, but the majority of state-of-the-art algorithms deal only with monotonic actions. However, some applications require agents to reason over different types Our application simulates reaction-based molecule generation, used as part drug discovery pipeline, includes uni-molecular bi-molecular reactions. This paper introduces a novel framework, towered actor critic (TAC), handle multiple action...
Modern structure–property models are widely used in chemistry; however, many cases, they still a kind of “black box” where there is no clear path from molecule structure to target property. Here we present an example deep learning usage not only build model but also determine key structural fragments ligands influencing metal complexation. We have series chemically similar lanthanide ions, and collected data on complexes’ stability, built models, predicting stability constants decoded the...