Abstract: A method for predicting a structure of a protein complex includes: obtaining an initial coordinate of each amino acid residue in a target protein complex, and obtaining a target residue pair feature, a first multiple sequence alignment (MSA) feature and a second MSA feature of each protein monomer in the target protein complex; and inputting the initial coordinate of each amino acid residue, and the target residue pair feature, the first MSA feature and the second MSA feature of each protein monomer into an N-level fold iteration network layer, and obtaining a target coordinate of each amino acid residue by predicting a torsion angle, a position transformation at residue level and a position transformation at monomer chain level of each amino acid residue via the N level fold iteration network layer, to obtain a predicted structure of the protein complex.

Abstract: A training method and apparatus for a full atomic structure prediction model. The method includes: obtaining structural information of a biomolecule and a first dynamic trajectory of the biomolecules; in which, the first dynamic trajectory includes position information of atoms in the biomolecule at different time points; adding noise to the first dynamic trajectory to obtain a second dynamic trajectory; encoding the structural information to obtain encoded features; decoding the encoded features and the second dynamic trajectory to obtain a target dynamic trajectory; and training an initial full atomic structure prediction model based on a difference between the target dynamic trajectory and the first dynamic trajectory, to obtain the full atomic structure prediction model.

Abstract: A method for information processing, is performed by an electronic device, and the method includes: obtaining a residue sequence AT that does not carry amino acid information and a first protein backbone structure BT generated by pure noise; and performing iterative denoising on the residue sequence AT and the first protein backbone structure BT; for a tth denoising, obtaining coevolution information of a residue sequence AT+1?t, and obtaining, based on the coevolution information and a first protein backbone structure BT+1?t, a residue sequence AT?t and a first protein backbone structure BT?t after the tth denoising, until the denoising is completed and a target amino acid sequence and a second protein backbone structure are obtained, where t is a positive integer, and 1?t?T, and T is a number of denoising times.

Abstract: A method and an apparatus for optimizing an mRNA sequence, an mRNA molecule, a pharmaceutical composition, and a use thereof are provided. The disclosure relates to the technical field of artificial intelligence, specifically to technical fields such as biological computing. The method for optimizing the mRNA sequence include: obtaining a first mRNA sequence for synthesizing a protein of interest, where the first mRNA sequence includes a 5? untranslated region sequence and a coding region sequence; and adjusting the 5? untranslated region sequence and the coding region sequence with the goal of maximizing a first score of the first mRNA sequence, so as to obtain an optimized second mRNA sequence for synthesizing the protein of interest, where the first score reflects at least one of the following indicators of the first mRNA sequence: translation initiation efficiency, codon adaptation index, and minimum free energy.

Abstract: In various examples, systems and methods are disclosed relating to geometric fabrics for accelerated policy learning and sim-to-real transfer in robotics systems, platforms, and/or applications. For example, a system can provide an input indicative of a goal pose for a robot to a model to cause the model to generate an output, the output representing a plurality of points along a path for movement of the robot to the goal pose; and generate one or more control signals for operation of the robot based at least on the plurality of points along the path and a policy corresponding to one or more criteria for the operation of the robot. In examples, the system can provide the one or more control signals to the robot to cause the robot to move toward the goal pose.

Abstract: Systems and techniques are described related to training one or more machine learning models for use in control of a robot. In at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.

Abstract: The present disclosure discloses a high-entropy alloy powder for laser cladding and a use method thereof. The alloy powder is CoCrFeMnNiCx, and x has a value of 0.1-0.15. The specific method includes: subjecting a 45 steel substrate to surface pretreatment, mixing the weighed CoCrFeMnNi high-entropy alloy powder with different content of a nano-C powder uniformly and pre-placed on the pre-treated substrate surface to form a prefabricated layer, then placing the prefabricated layer at 80-90° C. for constant temperature treatment for 8-12 h, and under a protective atmosphere, subjecting the cladding powder to laser cladding on the surface of the 45 steel. The method of the present disclosure prepares a CoCrFeMnNiCx high-entropy alloy coating with performance superior to the CoCrFeMnNi high-entropy alloy coating.

Abstract: Operation requests received from a tenant are added to a tenant-specific queue. A tenant scheduling work item is added to an execution queue that is shared with oilier tenants. When the tenant scheduling work item is executed, it copies up to a defined number of scheduled operations from the tenant-specific queue to the execution queue. The tenant-scheduling work item then re-adds itself to the execution queue. While the operations are executed and before the tenant scheduling work item is executed again, other tenants have an opportunity to queue their own operations. The tenant scheduling work item selects scheduled operations from the tenant-specific queue in the order they were originally requested until one of several conditions is met. Conditions may be based on how many operations are in progress, what kind of operations are in progress, and/or dependencies between operations of different types.

Abstract: The present disclosure discloses a high-entropy alloy powder for laser cladding and a use method thereof. The alloy powder is CoCrFeMnNiCx, and x has a value of 0.1-0.15. The specific method includes: subjecting a 45 steel substrate to surface pretreatment, mixing the weighed CoCrFeMnNi high-entropy alloy powder with different content of a nano-C powder uniformly and pre-placed on the pre-treated substrate surface to form a prefabricated layer, then placing the prefabricated layer at 80-90° C. for constant temperature treatment for 8-12 h, and under a protective atmosphere, subjecting the cladding powder to laser cladding on the surface of the 45 steel. The method of the present disclosure prepares a CoCrFeMnNiCx high-entropy alloy coating with performance superior to the CoCrFeMnNi high-entropy alloy coating.