Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
- Daniil Polykovskiy, Alexander Zhebrak, A. Zhavoronkov
- Computer ScienceFrontiers in Pharmacology
- 29 November 2018
A benchmarking platform called Molecular Sets (MOSES) is introduced to standardize training and comparison of molecular generative models and suggest to use the results as reference points for further advancements in generative chemistry research.
Deep biomarkers of human aging: Application of deep neural networks to biomarker development
- E. Putin, Polina Mamoshina, A. Zhavoronkov
- MedicineAging
- 1 May 2016
The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.
The role of DNA damage and repair in aging through the prism of Koch-like criteria
- A. Moskalev, M. Shaposhnikov, V. Fraifeld
- BiologyAgeing Research Reviews
- 1 March 2013
Applications of Deep Learning in Biomedicine.
- Polina Mamoshina, Armando Vieira, E. Putin, A. Zhavoronkov
- Computer ScienceMolecular Pharmaceutics
- 29 March 2016
Key features of deep learning that may give this approach an edge over other machine learning methods are discussed and a number of applications ofdeep learning in biomedical studies demonstrating proof of concept and practical utility are reviewed.
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.
- A. Aliper, S. Plis, Artem V. Artemov, Alvaro E. Ulloa, Polina Mamoshina, A. Zhavoronkov
- Computer ScienceMolecular Pharmaceutics
- 8 June 2016
This work demonstrates a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions and proposes using deep neural net confusion matrices for drug repositioning.
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
- A. Zhavoronkov, Y. Ivanenkov, Alán Aspuru-Guzik
- BiologyNature Biotechnology
- 1 September 2019
A machine learning model allows the identification of new small-molecule kinase inhibitors in days and is used to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days.
Bifunctional immune checkpoint-targeted antibody-ligand traps that simultaneously disable TGFβ enhance the efficacy of cancer immunotherapy
It is demonstrated that Y-traps counteract TGFβ-mediated differentiation of Tregs and immune tolerance, thereby providing a potentially more effective immunotherapeutic strategy against cancers that are resistant to current immune checkpoint inhibitors.
Human-specific endogenous retroviral insert serves as an enhancer for the schizophrenia-linked gene PRODH
- M. Suntsova, E. Gogvadze, A. Buzdin
- BiologyProceedings of the National Academy of Sciences
- 11 November 2013
A systematic, whole-genome analysis of enhancer activity of human-specific endogenous retroviral inserts (hsERVs) identified an element, hsERVPRODH, that acts as a tissue-specific enhancer for the PRODH gene, which is required for proper CNS functioning and is proposed to have contributed to human CNS evolution.
Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare
- Polina Mamoshina, Lucy Ojomoko, A. Zhavoronkov
- Computer ScienceOncoTarget
- 7 November 2015
An overview of the next-generation artificial intelligence and blockchain technologies is provided and innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring are presented.
Reinforced Adversarial Neural Computer for de Novo Molecular Design
- E. Putin, Arip Asadulaev, A. Zhavoronkov
- Computer ScienceJournal of Chemical Information and Modeling
- 15 May 2018
An original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on the generative adversarial network (GAN) paradigm and reinforcement learning (RL).
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