A composition-of-matter comprising a crystallized form of a large ribosomal (50S) subunit of a pathogenic bacterium, and the atomic coordinates of the three-dimensional structure thereof are provided herein, as well as methods for crystallizing the same, and using the atomic coordinates of the same to design de novo ligands with high specificity thereto.

Described in certain example embodiments herein are systems, methods, and uses thereof for high-throughput in vitro evaluating multiple test compounds in parallel for biological or pharmacological functions. In certain embodiments, the system allows the selection of a subset of test compounds from a group of test compounds to form an optimized pool, and methods are provided to use such optimized pool of test compounds to identify and validate therapeutic agents for treating diseases and driving guided differentiation of stem cells into desired types of cells. The systems described herein can provide, for example, a cost-effective and high-quality high-throughput approach for drug screening.

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

A drug-screening system includes an encoding module, a candidate-drug generating module and a drug-ranking module. The encoding module is configured to encode a drug expression and at least one drug-ranking indicator to generate a first encoding variable. The candidate-drug generating module is configured to train a generative adversarial network according to the first encoding variable to generate a plurality of candidate drugs, wherein each of the candidate drugs has a generative drug expression and at least one generative drug-ranking indicator. The drug-ranking module is configured to rank strengths of the candidate drugs according to the generative drug-ranking indicator of each of the candidate drugs.

The present disclosure relates to a chromatographic analysis system, a chromatogram detection and analysis method and an electronic device, belonging to that technical field of chromatographic analysis, wherein the system comprises a data acquiring unit, which is configured to acquire raw data generated by a chromatographic analysis instrument; an analysis processing unit, which is configured to analyze the raw data based on the analysis operation configured by a user to obtain analysis data; a storing and managing unit, which is configured to store raw data and analysis data, and store method parameters and management data of the analysis system; a client unit, which is configured to provide the interactive interface of the system to a user; wherein the storing and managing unit comprises a first database for storing raw data and analysis data and a second database for storing method parameters and management data of the analysis system. The present disclosure is beneficial to ensuring the integrity and security of the acquired raw data and meeting the new demands in the industry practice.

The present disclosure relates to a chromatographic analysis system, a chromatogram detection and analysis method and an electronic device, belonging to that technical field of chromatographic analysis, wherein the system comprises a data acquiring unit, which is configured to acquire raw data generated by a chromatographic analysis instrument; an analysis processing unit, which is configured to analyze the raw data based on the analysis operation configured by a user to obtain analysis data; a storing and managing unit, which is configured to store raw data and analysis data, and store method parameters and management data of the analysis system; a client unit, which is configured to provide the interactive interface of the system to a user; wherein the storing and managing unit comprises a first database for storing raw data and analysis data and a second database for storing method parameters and management data of the analysis system. The present disclosure is beneficial to ensuring the integrity and security of the acquired raw data and meeting the new demands in the industry practice.

A drug library management system facilitates centralized management of the drug libraries that are used by various infusion pumps, including in clinical environments that have different types and/or versions of infusion pumps. Medications, administration rules, critical care area rules, and the like can be maintained using the drug library management system. The drug library management system can generate and distribute drug library data in pump-specific formats or other customized formats as needed.

A method of discovery a drug based on bioactivity data includes extracting, by the analysis apparatus, bioassay data from a bioassay database, classifying, by the analysis apparatus, a plurality of candidate compounds included in the bioassay data into a similar compound group and a dissimilar compound group based on similarity with the target compound, calculating, by the analysis apparatus, a relative activity score (RAS) based on activity information on compounds belonging to the similar compound group and the dissimilar compound group; and selecting, by the analysis apparatus, at least some of the plurality of candidate compounds included in the bioassay data as a drug candidate substance based on the RAS.

The disclosure provides methods and systems for identifying a subset of compounds in a plurality of compounds. The identifying includes obtaining, for each compound, a vector including a set of elements, where each element includes a measurement of a different feature of an instance of a cell context upon exposure to the compound. The identifying includes performing the obtaining for a plurality of cell contexts, to obtain a plurality of vectors for each compound across different cell contexts. The identifying includes combining the vectors for each compound to form a combined vector for each compound, thereby forming a plurality of combined vectors representing different compounds. The identifying includes pruning the plurality of compounds to the subset of compounds based on a similarity between respective combined vectors in the plurality of combined vectors corresponding to compounds in the plurality of compounds.

A method of designing a D-polypeptide that binds with an L-target protein can include: identifying a polypeptide target having L-chirality; determining hotspot amino acids of a polypeptide ligand having L-chirality that have binding interactions with the L-target protein; determining transformations of side chains of the hotspot amino acids that retain the binding interactions with the target; generating inversed hotspot amino acids with chirality opposite to the one of the target; identifying a polypeptide having inverse chirality from the target protein, on which a combination of inversed hotspot amino-acid can be grafted without significantly changing their interactions with the target. The designed ligands can be processed and converted to D-ligands that bind with the L-target protein.