Искусственный интеллект для органической и медицинской химии
Синтелли - это платформа искусственного интеллекта для изучения химического пространства, прогнозирования свойств органических соединений и многого другого. Попробуйте с нашим тарифным планом Free
Исследуйте химическое пространство, используя наши модели глубокого обучения. Анализ кластеров биоактивных соединений. Генерация новых соединений с заданными свойствами.
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.
Struct2IUPAC — Transformer-Based Artificial Neural Network for the Conversion Between Chemical Notations
We developed a Transformer-based artificial neural architecture to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct.
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).
A Survey of Multi-task Learning Methods in Chemoinformatics
We review the recent developments in multi-learning approaches as well as cover the freely available tools and packages that can be used to perform such studies.
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.
Algorithm learns nomenclature to name chemical compounds
Scientists developed the first open-source tool to translate chemical structures to their IUPAC names using machine learning software designed by Google
Scientists present an effective drug toxicity prediction method
Researchers have developed an enhanced drug candidate toxicity prediction technology based on multi-task machine learning algorithms and analysis of various types of toxicity data