Используйте блок прогнозирования свойств для органических молекул, которых нет в нашей базе: физико-химические, биологические свойства, токсичность, канцерогенность, сложность синтеза, прогноз IUPAC имен
We present a Transformer-based artificial neural network to convert images of organic structures to molecular structures. We demonstrate that the Transformer-based architecture can gather chemical insights from our generator with almost absolute confidence.
Exploring Chemical Reaction Space with Reaction Difference Fingerprints and Parametric t-SNE
We demonstrated that the parametric t-SNE combined with reaction difference fingerprints could provide a tool for the projection of chemical reactions onto a low-dimensional manifold for easy exploration of reaction space.
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space
We performed a comparative study of prediction multitask toxicity for a broad chemical space using different descriptors and modeling algorithms and applied multitask learning for a large toxicity data set extracted from the Registry of Toxic Effects of Chemical Substances (RTECS).
3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction
We present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input.
PyFragMS ─ A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways
We have created PyFragMS - a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or 16O/18O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule.
Biphenyl scaffold for the design of NMDA-receptor negative modulators: molecular modeling, synthesis, and biological activity
We present here the activity optimization process of a biphenyl-based NMDA negative allosteric modulator (NAM) guided by free energy calculations, which led to a 100 times activity improvement (IC50 = 50 nM) compared to a hit compound identified in virtual screening.