V
Vladimir A. Aladinskiy
Researcher at Moscow Institute of Physics and Technology
Publications - 26
Citations - 1829
Vladimir A. Aladinskiy is an academic researcher from Moscow Institute of Physics and Technology. The author has contributed to research in topics: Antibacterial activity & Computer science. The author has an hindex of 9, co-authored 23 publications receiving 957 citations. Previous affiliations of Vladimir A. Aladinskiy include Skolkovo Institute of Science and Technology.
Papers
More filters
Journal ArticleDOI
Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
Alex Zhavoronkov,Yan A. Ivanenkov,Alexander Aliper,Mark S. Veselov,Vladimir A. Aladinskiy,Anastasiya V Aladinskaya,Victor A Terentiev,Daniil Polykovskiy,Maksim Kuznetsov,Arip Asadulaev,Yury Volkov,Artem Zholus,Rim Shayakhmetov,Alexander Zhebrak,Lidiya I Minaeva,Bogdan A Zagribelnyy,Lennart H Lee,Richard Soll,David Madge,Li Xing,Tao Guo,Alán Aspuru-Guzik +21 more
TL;DR: 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.
Journal ArticleDOI
Reinforced Adversarial Neural Computer for de Novo Molecular Design
Evgeny Putin,Arip Asadulaev,Yan A. Ivanenkov,Yan A. Ivanenkov,Vladimir A. Aladinskiy,Benjamin Sanchez-Lengeling,Alán Aspuru-Guzik,Alán Aspuru-Guzik,Alex Zhavoronkov +8 more
TL;DR: 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).
Posted Content
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Daniil Polykovskiy,Alexander Zhebrak,Benjamin Sanchez-Lengeling,Sergey Golovanov,Oktai Tatanov,Stanislav Belyaev,Rauf Kurbanov,Aleksey Artamonov,Vladimir A. Aladinskiy,Mark S. Veselov,Artur Kadurin,Simon Johansson,Hongming Chen,Sergey I. Nikolenko,Alán Aspuru-Guzik,Alex Zhavoronkov +15 more
TL;DR: 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.
Journal ArticleDOI
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models.
Daniil Polykovskiy,Alexander Zhebrak,Benjamin Sanchez-Lengeling,Sergey Golovanov,Oktai Tatanov,Stanislav Belyaev,Rauf Kurbanov,Aleksey Artamonov,Vladimir A. Aladinskiy,Mark S. Veselov,Artur Kadurin,Simon Johansson,Hongming Chen,Sergey I. Nikolenko,Alán Aspuru-Guzik,Alex Zhavoronkov +15 more
TL;DR: MOSES as mentioned in this paper is a benchmarking platform for molecular generative models, which provides training and testing datasets and a set of metrics to evaluate the quality and diversity of generated structures.
Journal ArticleDOI
Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
Daniil Polykovskiy,Alexander Zhebrak,Dmitry Vetrov,Yan A. Ivanenkov,Yan A. Ivanenkov,Vladimir A. Aladinskiy,Polina Mamoshina,Marine E. Bozdaganyan,Alexander Aliper,Alex Zhavoronkov,Artur Kadurin +10 more
TL;DR: A new generative architecture is proposed, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against a specific protein, solubility, or ease of synthesis, that is applied to generate a novel inhibitor of Janus kinase 3.