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Analysis

1 / 4,154
1.WO/2026/099209A METHOD FOR COMPENSATING THE EFFECTS OF GAS CONTAMINATION FOR A VENOUS BLOOD SAMPLE
WO - 15.05.2026
Int.Class G16C 60/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
60Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
 Appl.No PCT/EP2025/081855 Applicant AALBORG UNIVERSITET Inventor REES, Stephen Edward
) together with the blood. The method has a mathematical model with physiological sub-models simulating development in blood together with this residual gas, including sub-models describing: 1) the diffusion of gasses between the residual gas and the blood sample, 2) the effects of the diffusing gas in the blood in the vacuum tube, and 3) the effects of the diffusing gas in the residual gas in the vacuum tube. The mathematical model then calculates a modified set of blood variables (BV) to compensate for the physical and/or chemical interactions between the residual gasses and the blood sample in the time following sampling and before blood gas measurement. By accounting for this contamination of the blood sample, calculation of the actual values of blood variables seen in the patient is possible.
2.WO/2026/098969TRAINING GENERATIVE MACHINE LEARNING MODELS ON MOLECULAR DYNAMICS SIMULATION DATA
WO - 15.05.2026
Int.Class G16C 20/70
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
70Machine learning, data mining or chemometrics
 Appl.No PCT/EP2025/080557 Applicant ISOMORPHIC LABS LIMITED Inventor WIRNSBERGER, Peter
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generative machine learning model In one aspect, a method comprises: obtaining a set of training examples for training a generative machine learning model to perform a structure prediction task; augmenting the set of training examples to include a plurality of new training examples, comprising, for each training example in the set of training examples: performing a molecular dynamics simulation of a molecular system, starting from a conformation of the molecular system that is specified by the training example, to generate a trajectory of new conformations adopted by the molecular system over a duration of time; generating one or more new training examples corresponding to the molecular system; and training the generative machine learning model to perform the structure prediction task on the augmented set of training examples.
3.WO/2026/099134PHYSICS-BASED GUIDANCE FOR MOLECULAR STRUCTURE PREDICTION USING GENERATIVE DIFFUSION MODELS
WO - 15.05.2026
Int.Class G16B 15/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
15ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
30Drug targeting using structural data; Docking or binding prediction
 Appl.No PCT/EP2025/081712 Applicant ISOMORPHIC LABS LIMITED Inventor REDDY, Siddharth Gajjala
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a predicted 3D structure of a molecular system In one aspect, a method comprises using a generative diffusion model to generate the predicted 3D structure of the molecular system by denoising 3D structure data for the molecular system over a sequence of denoising iterations, comprising, at each of a plurality of denoising iterations: processing current structure data for the molecular system using a physics scoring model to generate a physics score that characterizes a degree to which the current structure data satisfies one or more physical constraints on the 3D structure of the molecular system; and generating a respective physics adjustment to the spatial position for each of the plurality of atoms based on the physics score.
4.WO/2026/101398BARCODE FREE LIBRARY SCREEN
WO - 15.05.2026
Int.Class G01N 33/68
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
68involving proteins, peptides or amino acids
 Appl.No PCT/NL2025/050557 Applicant UNIVERSITEIT LEIDEN Inventor POMPLUN, Sebastian Johannes
The present invention relates to a method for identifying which members of a barcode-free library of small molecules bind to a target and elucidating their molecular structure, the method comprising: a) mixing the members of at least one subset of the library with the target; b) separating the members that do not bind to the target (nonbinders) from the members that bind to the target (binders); c) analysing the binders using tandem mass spectrometry (MS/MS) to generate MS/MS spectra of the binders; and d) analysing the MS/MS spectra by computational analysis to elucidate the molecular structure of the binders.
5.WO/2026/099145IDENTIFICATION OF EMERGING NEEDS
WO - 15.05.2026
Int.Class G16C 20/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
30Prediction of properties of chemical compounds, compositions or mixtures
 Appl.No PCT/EP2025/081738 Applicant BASF SE Inventor MARTSCHAT, Sebastian Hermann
A method, in particular computer-implemented method, for obtaining a chemical product with a desired property, the method comprising: - obtaining, in particular receiving one or more chemical product data set(s) related to an application of the chemical product, wherein the one chemical product data set(s) comprise one or more element(s), - selecting one or more chemical product data set(s) according to an occurrence of the one or more element(s) per chemical product data set, - determining one or more desired properties of the chemical product per selected chemical product data set, - providing the one or more desired properties of the chemical product for providing, in particular producing, the chemical product associated with the one or more desired properties.
6.WO/2026/097942POLYOLEFIN PRODUCTION PARAMETER OPTIMIZATION METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIUM
WO - 15.05.2026
Int.Class G16C 20/10
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
10Analysis or design of chemical reactions, syntheses or processes
 Appl.No PCT/CN2025/110516 Applicant EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY Inventor DU, Wenli
The present application relates to the technical field of automation, and specifically, to a polyolefin production parameter optimization method and apparatus, a device, and a storage medium, with the aim to improve polyolefin production parameter optimization efficiency. The method comprises: for historical production parameter sets corresponding to a plurality of historical polyolefin categories, respectively constructing historical product prediction models, and iteratively performing parameter optimization processing on a parameter set to be optimized of a target polyolefin category by means of the historical product prediction models until the parameter set to be optimized meets a target category production condition, so as to obtain a target production parameter set. Each iterative training involves: performing predictive and comparative processing on the parameter set to be optimized by means of each historical product prediction model so as to obtain a similarity weight of each historical product prediction model; determining a recommended production parameter of the target polyolefin category according to each resulting similarity weight; updating the parameter set to be optimized according to the recommended production parameter, and performing the next parameter optimization processing iteration.
7.WO/2026/099202A METHOD FOR COMPENSATING THE EFFECTS OF TIME DELAY FROM SAMPLING TO MEASUREMENT IN A BLOOD SAMPLE
WO - 15.05.2026
Int.Class G16C 60/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
60Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
 Appl.No PCT/EP2025/081838 Applicant AALBORG UNIVERSITET Inventor REES, Stephen Edward
The invention relates to a method for compensating the effects of time delay (TD) between sampling and actual measurement of acid-base, oxygenation and/or electrolyte status in a blood sample from a subject, such as a human patient. A mathematical model is simulating time development in the blood sample, the mathematical model having 1) a first sub-model describing red blood cell metabolism, and 2) a second sub-model describing the effect of the red blood cell metabolism on the whole blood acid-base chemistry. The mathematical model calculates from a time of measurement (Tm) to the time of sampling (Ts) a modified set of blood variables to compensate for a time delay (TD), e.g. by calculating backwards in time 0.5-3 hours. The invention can simulate the biochemical processes occurring in blood, and demonstrates the ability to simulate changes in measured blood variables over time. This may simplify transport and/or storage constraints of blood samples, and improve quality and accuracy of blood gas measurements.
8.WO/2026/098421MATERIAL CROSS-SECTION LIBRARY CONSTRUCTION METHOD, DOSE ASSESSMENT METHOD, AND SYSTEM
WO - 15.05.2026
Int.Class G16C 60/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
60Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
 Appl.No PCT/CN2025/132262 Applicant NEUBORON THERAPY SYSTEM LTD. Inventor ZHONG, Wan-bing
A material cross-section library construction method, a dose assessment method, and a system. The material cross-section library construction method comprises: setting a material energy node; acquiring material information, the material information at least comprising constituent nuclides and nuclide particle number density; and constructing a material cross-section library on the basis of the material energy node and the material information. The dose assessment method further comprises: performing particle simulation calculation on the basis of the material cross-section library to obtain a cross-section value of a material where particles are located; and assessing a dose on the basis of the cross-section value of the material where the particles are located.
9.WO/2026/093340SYNTHETIC DATA GENERATION USING MATCHED MOLECULAR PAIRS
WO - 07.05.2026
Int.Class G16C 20/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
30Prediction of properties of chemical compounds, compositions or mixtures
 Appl.No PCT/EP2025/081180 Applicant ISOMORPHIC LABS LIMITED Inventor RICHARDS, Simon James
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating synthetic training examples for training a machine learning model. In one aspect, a method comprises: processing the data identifying a collection of molecules to identify a plurality of matched molecular pairs; processing the plurality of matched molecular pairs to generate data defining a set of transformation functions, wherein each transformation function is defined by at least: (i) a first molecular fragment; (ii) a second molecular fragment; (iii) an inclusion criterion defining a class of molecules; and (iv) a predicted change in the molecular property value resulting from replacing the first molecular fragment by the second molecular fragment in a molecule that satisfies the inclusion criterion; generating a plurality of synthetic training examples for training a machine learning model using the set of transformation functions.
10.20260127853METHODS AND SYSTEMS FOR CHEMISTRY-AWARE AUTOMATED CLASSIFICATION OF AND INIHIBITATION PROTOCOLS FOR CORROSION AND MATERIAL DEGRADATION
US - 07.05.2026
Int.Class G06V 10/764
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
10Arrangements for image or video recognition or understanding
70using pattern recognition or machine learning
764using classification, e.g. of video objects
 Appl.No 18938868 Applicant Toyota Research Institute, Inc. Inventor Steven Bartholomew Joseph Torrisi

An automated corrosion prediction system includes a processor and a memory communicably coupled to the processor and storing machine-readable instructions. The machine-readable instructions, when executed by the processor, cause the processor to train a multimodal machine learning (ML) model with data from a plurality of corrosion-related databases, execute an image analysis of an image of a corrosion product on a substrate and identify features of the image, map the image analysis to a description of the identified features, provide the description to the trained multimodal ML model, and automatically predict, within 60 seconds from receiving the description, at least one of a type and a chemistry of the corrosion product from the description using the trained multimodal ML model.

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