Искусственный интеллект для органической и медицинской химии
Синтелли - это платформа искусственного интеллекта для изучения химического пространства и прогнозирования свойств органических соединений. 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.
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.
We developed a Transformer-based artificial neural architecture to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct.
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).
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.
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.
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.
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.
Scientists developed the first open-source tool to translate chemical structures to their IUPAC names using machine learning software designed by Google
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
Scientists evaluated the ability of artificial intelligence that suggest products to buy and recommend new antiviral compounds
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