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1 / 3,500
1.20250285712METHOD AND APPARATUS FOR PREDICTING PROTEIN-LIGAND BINDING FREE ENERGY
US - 11.09.2025
Int.Class G16C 10/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
10Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
 Appl.No 18598226 Applicant INCEREBRO, INC. Inventor Eun Sung CHO

Provided are a method and apparatus for predicting protein-ligand binding free energy. The method includes generating a highest-probability pose for a selected protein-ligand set using a quantum mechanics-based Mining Minima algorithm, replacing a charge value of the pose with atomic electrostatic potential charge (QESP) using a quantum mechanical calculation method predicting the atomic charges of protein residues and ligands existing in a quantum mechanical domain, and predicting the protein-ligand binding free energy using the charge value-replaced pose.

2.WO/2025/186623SYSTEM, METHOD, AND COMPUTER READABLE MEDIUM CONFIGURED TO USE MACHINE LEARNING MODELS TO DETERMINE OPTIMAL SYNTHETIC ROUTES
WO - 11.09.2025
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/IB2025/000094 Applicant PREPAIRE LABS LIMITED Inventor FREER, Carl
A subject matter of the present invention is system, method, and computer readable medium configured to synthesize a drug product. Single-step organic-chemistry reactions are provided as inputs to a first machine learning model. At least one synthetic route to a product is determined based on an output of the first machine learning model. A score for each of the at least one synthetic route is determined. The at least one synthetic route and its respective score are provided as inputs to a second machine learning model. At least one qualified route is determined based on an output of the second machine learning model.
3.WO/2025/185664MOLECULAR SIMILARITY CALCULATION METHOD AND APPARATUS BASED ON PHOTONIC QUANTUM COMPUTER
WO - 11.09.2025
Int.Class G06F 18/22
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
18Pattern recognition
20Analysing
22Matching criteria, e.g. proximity measures
 Appl.No PCT/CN2025/080813 Applicant BEIJING QBOSON QUANTUM TECHNOLOGY CO., LTD. Inventor WEN, Kai
The present invention relates to the technical field of drug design, and in particular to a molecular similarity calculation method based on a photonic quantum computer, comprising: on the basis of molecular structures, respectively converting two molecules requiring molecular similarity calculation into molecular graphs, wherein vertexes of each molecular graph represent atoms in the corresponding molecule, and edges of the molecular graph represent chemical bond connections between the atoms in the molecule; using the two molecular graphs to construct a conflict graph, wherein vertexes of the conflict graph are derived from pairwise vertex matching mappings between the two molecular graphs, and edges of the conflict graph are derived from discrimination results between the vertices in the conflict graph; solving a maximum weighted independent subset of the conflict graph by means of a photonic quantum computer and obtaining information of each vertex of the conflict graph in the maximum weighted independent subset; and calculating the similarity between the two molecules on the basis of the information of each vertex of the conflict graph in the maximum weighted independent subset. The method overcomes the defects in conventional solving approaches of low solving speed and high false positive rate, effectively improving the calculation speed and accuracy.
4.112023004734LERNVORRICHTUNG, TRAINERTES MODELL, VORHERSAGEVORRICHTUNG, VORHERSAGEPROGRAMM UND VORHERSAGEVERFAHREN
DE - 11.09.2025
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 112023004734 Applicant ENEOS CORPORATION Inventor Onodera Tasuku

Lernvorrichtung, die ein Trainingselement aufweist, das konfiguriert ist, durch Training ein trainiertes Modell unter Verwendung eines Trainingsdatensatzes zu erzeugen, in dem interessierende Moleküle, die Moleküle sind, die in einem Basisöl enthalten sind, das bei einer Normaltemperatur von 20 °C und einem Normaldruck von 1,013 × 105 Pa flüssig ist, und ein Merkmal, das einen intermolekularen Raum zwischen den interessierenden Molekülen in einer Simulationszelle mit einer frei gewählten Parallelepiped-Form angibt, miteinander assoziiert sind. embedded image

5.WO/2025/186208CUSTOMIZED FORMULATIONS
WO - 11.09.2025
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/EP2025/055757 Applicant BASF SE Inventor BHATTACHARYA, Sandip
The present disclosure relates to the production of cosmetic formulations with specified target properties. Disclosed are methods, apparatuses, systems for determining the stability of the formulation and specified target properties as well as generating formulation data for producing the formulation. Further disclosed are cosmetic products and cosmetic formulations produced based on the generated formulation control data.
6.WO/2025/186253METHOD, SYSTEM AND COMPUTER-READABLE STORAGE MEDIUM FOR MYCELIUM BIOCOMPOSITE PROPERTY PREDICTION AND METHOD, SYSTEM AND COMPUTER-READABLE STORAGE MEDIUM FOR PROCESSING MYCELIUMELECTROGRAPHY SIGNALS
WO - 11.09.2025
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/EP2025/055845 Applicant CASILLAS PACHECO, Ruben Alfonso Inventor CASILLAS PACHECO, Ruben Alfonso
The present disclosure is directed to a method, system and computer-readable storage medium for mycelium biocomposite property prediction. The method can include selecting target properties for a MyBC in one or several of its development phases. The method can further include inputting the target properties into a trained ML model, wherein the output of the trained ML model comprises a prediction of development phases key features such as formulation, organic, material and digital that has the highest probability to lead to the target properties. Furthermore, the present disclosure is directed to a method, system and computer-readable storage medium for processing myceliumelectrography signals.
7.20250285706WATER-SOLUBLE TRANS-MEMBRANE PROTEINS AND METHODS FOR THE PREPARATION AND USE THEREOF
US - 11.09.2025
Int.Class G16B 15/20
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
20Protein or domain folding
 Appl.No 19016282 Applicant Massachusetts Institute of Technology Inventor Shuguang Zhang

The present invention is directed to water-soluble membrane proteins, methods for the preparation thereof and methods of use thereof.

8.20250285714A PHARMACEUTICAL PLATFORM TECHNOLOGY FOR DRUG DISCOVERY AND CONSUMER HEALTH PRODUCT DEVELOPMENT
US - 11.09.2025
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 18684451 Applicant SINOVEDA CANADA INC. Inventor Yun Kau TAM

In one embodiment, the present invention describes a method for identifying an optimized natural medicine containing defined doses of active and contributing ingredients. In one embodiment, the method disclosed in the present invention develops compositions comprising cannabinoids for the treatment of hepatocellular carcinoma.

9.20250285715IDENTIFYING DRIVERS OF MOLECULE TOXICITY USING TOXICITY ANALYSIS TREES
US - 11.09.2025
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 19009903 Applicant AXIOMBIO, INC. Inventor Brandon White

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of a molecule. In one aspect, a method comprises: obtaining data identifying an input molecule; generating data defining a toxicity analysis tree for the input molecule; and processing the toxicity analysis tree to generate a respective toxicity score for each of a plurality of molecule fragments in the input molecule that characterizes an impact of the molecule fragment on a toxicity of the input molecule.

10.WO/2025/188250COLLISION ENERGY SPECIFIC TANDEM MASS SPECTRAL PREDICTION
WO - 11.09.2025
Int.Class G01N 27/62
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
27Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
62by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
 Appl.No PCT/SG2025/050164 Applicant AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH Inventor TAY, Dillon Wei Peng
A model is trained to predict collision-energy specific tandem mass spectra (MS/MS) for molecules. Directed acyclic graphs (DAG) are generated, that structurally relates predicted fragments for each molecule. A fragmentation model is then trained based on the DAGs and respective molecular inputs, to generate most probable fragments based on molecular inputs. The input data is then binned, with each bin corresponding to a non-overlapping collision energy range. The trained fragmentation model is frozen and intensity models (one for each bin) are trained to predict MS/MS spectra for molecules based on fragments generated by the fragmentation model.
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