A computer-implemented method for predicting a conformation of a ligand docked into a protein is disclosed. According to some embodiments, the method may include determining one or more poses of the ligand in the protein, the poses being representative conformations of the ligand. The method may also include determining, using a neural network, energy scores of the poses. The method may further include determining a proper conformation for the docked ligand based on the energy scores.

Disclosed are methods for identifying bio-molecules with desired properties (or which are most suitable for a round of directed evolution) from complex bio-molecule libraries or sets of such libraries. Some embodiments of the present disclosure provide methods for virtually screening proteins for beneficial properties. Some embodiments of the present disclosure provide methods for virtually screening enzymes for desired activity and/or selectivity for catalytic reactions involving particular substrates. Some embodiments combine screening and directed evolution to design and develop proteins and enzymes having desired properties. Systems and computer program products implementing the methods are also provided.

A method and system for in-silico testing of actives on human skin is described. The present invention discloses a micro and macroscopic level model of the skins upper protective layer Stratum-Corneum. The invention presents a multi-scale modeling framework for the calculation of diffusion and release profile of different actives like drugs, particles and cosmetics through developed skin model using molecular dynamics simulations and computational fluid dynamics approach. The systems consist of a molecular model of the skin's upper layer stratum corneum and permeate molecules. The system also consists of a macroscopic transport model of stratum corneum. The transport model is used to generate the release profile of the active molecule.

A method and system for in-silico testing of actives on human skin is described. The present invention discloses a micro and macroscopic level model of the skins upper protective layer Stratum-Corneum. The invention presents a multi-scale modeling framework for the calculation of diffusion and release profile of different actives like drugs, particles and cosmetics through developed skin model using molecular dynamics simulations and computational fluid dynamics approach. The systems consist of a molecular model of the skin's upper layer stratum corneum and permeate molecules. The system also consists of a macroscopic transport model of stratum corneum. The transport model is used to generate the release profile of the active molecule.

The present disclosure relates to inhibitors of mitochondrial function. Methods of screening compounds for mitochondrial inhibition are disclosed. Also described are methods of using mitochondrial inhibitors called mitoriboscins—mitochondrial-based therapeutic compounds having anti-cancer and antibiotic properties—to prevent or treat cancer, bacterial infections, and pathogenic yeast, as well as methods of using mitochondrial inhibitors to provide anti-aging benefits. Specific mitoriboscin compounds and groups of mitoriboscins are also disclosed.

The present invention concerns screening methods to identify compounds that regulate activity of RSH enzymes such as Rel, and specifically Rel synthetase and/or Rel hydrolase activity. Also intended are compounds that interact and regulate Rel synthetase and/or hydrolase activity. These compounds are valuable to target persister cells not affected by traditional antibiotics.

The present invention concerns screening methods to identify compounds that regulate activity of RSH enzymes such as Rel, and specifically Rel synthetase and/or Rel hydrolase activity. Also intended are compounds that interact and regulate Rel synthetase and/or hydrolase activity. These compounds are valuable to target persister cells not affected by traditional antibiotics.

The disclosure relates to novel selective Grp94 inhibitors, compositions comprising an effective amount of such compounds, and methods to treat or prevent a condition, such as cancer, comprising administering to an animal in need thereof an effective amount of such compounds.

The disclosure relates to novel selective Grp94 inhibitors, compositions comprising an effective amount of such compounds, and methods to treat or prevent a condition, such as cancer, comprising administering to an animal in need thereof an effective amount of such compounds.

Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to N-acylimines.