Insilico Medicine IP Limited

Yan A Ivanenkov

Aleksandr M Aliper

Daniil Polykovskiy

Aleksandrs Zavoronkovs

Evgeny Putin

Alexander Zhebrak

Maksim Kuznetsov

Mark Veselov

Yuriy Volkov

Arip Asadulaev

Feng Ren

Xiao Ding

Vladimir Aladinskiy

Jingjing Peng

Bogdan Zagribelnyy

Anton Konstantinov

Anton S Vantskul

Victor Terentiev

Xiaoyu Ding

A synthesis protocol for a reaction pathway of a target molecule can be determined by: providing target compound data; performing a chemical synthesis search for at least one reaction pathway for the target compound; processing the target compound data through a single-step reaction enumeration algorithm to obtain at least one reaction step of the least one reaction pathway; processing at least one reaction step with the at least one reaction pathway scoring mechanism model to obtain a reaction step score; constructing reaction pathways based on at least one reaction step and at least one reaction step score; providing a selectivity filter having a selectivity criteria; filtering the reaction pathways so that reactions violating the selectivity criteria is filtered out; ranking the reaction pathways; and providing the reaction pathway ranking.

Retrosynthesis systems and methods

A computer-implemented method can include: receiving input of a biological target; receiving a generative model (e.g., tensorial reinforcement learning (GENTRL) model or other model) trained with reference compounds, wherein the reference compounds include: general compounds, compounds that modulate the biological target, and compounds that modulate biomolecules other than the biological target; generating structures of generated compounds with the generative model; prioritizing structures of generated compounds based on at least one criteria; processing prioritized chemical structures of the generated compounds through a Sammon mapping protocol to obtain hit structures; and providing chemical structures of the hit structures. One or more non-transitory computer readable media are provided that store instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising performing the computer methods described herein for providing chemical structure of hit structures generated by the generative model.

Workflow for generating compounds with biological activity against a specific biological target

Provided herein are compounds of Formulas (I), (II), (III), and (IV) and subformulas thereof, wherein the variables are defined herein. Also provided herein are pharmaceutical compositions comprising a compound of Formula (I), (II), (III), or (IV) and methods of using the compounds, e.g., in the treatment of immune disorders, inflammatory disorders, and cancer.

Bycyclic JAK inhibitors and uses thereof

The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Subset conditioning using variational autoencoder with a learnable tensor train induced prior

Provided herein are compounds, pharmaceutical compositions, and methods for treating a SARS-CoV-2 infection.

SARS-CoV-2 inhibitors for treating coronavirus infections

A DDR1 inhibitor compound can have a structure of Formula A, derivative thereof, prodrug thereof, salt thereof, stereoisomer thereof, tautomer thereof, polymorph thereof, or solvate thereof, or having any chirality at any chiral center, 

Kinase inhibitors

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Moscow, Russia

Yan A Ivanenkov