An initial conformation generation apparatus includes one or more memories; and one or more processors coupled to the one or more memories and the one or more processors configured to generate a model representing a cyclic peptide molecule by identifying Cα atoms of each of a plurality of amino acid residues, by arranging the identified Cα atoms on a circumference, and by adding main chains and side chains of the plurality of amino acid residues, and search for a stable conformation of the cyclic peptide molecule by using the generated model as an initial conformation of the cyclic peptide molecule.

An electronic device obtains structural data of a reference molecule. The electronic device performs structural separation on the structural data of the reference molecule to obtain group data of a molecular segment group corresponding to the reference molecule. The electronic device performs feature processing on the group data of the molecular segment group to obtain a candidate segment for replacing the fragment segment. The electronic device generates structural data of a reconstructed molecule based on the candidate segment and the side chain segment.

An electronic device obtains structural data of a reference molecule. The electronic device performs structural separation on the structural data of the reference molecule to obtain group data of a molecular segment group corresponding to the reference molecule. The electronic device performs feature processing on the group data of the molecular segment group to obtain a candidate segment for replacing the fragment segment. The electronic device generates structural data of a reconstructed molecule based on the candidate segment and the side chain segment.

This disclosure provides new methods for the design and development of ethylene polymerization catalysts, including Group 4 metallocene catalysts such as zirconocenes, which are based on an improved ability to adjust co-monomer incorporation into the polymer. Computational analyses with and without dispersion corrections revealed that designing catalyst scaffolds which induce stabilizing non-covalent dispersion type interactions with incoming α-olefin co-monomers can be used to modulate co-monomer selectivity into the polyethylene chain. Demonstrated herein is a lack of correlation of computed ΔΔG.sup.‡ values against experimental ΔΔG.sup.‡ values when the dispersion correction (D3BJ) was disabled, and B3LYP was used in the absence of Grimme's D3 dispersion and Becke-Johnson (BJ) dampening, but a correlation of computed against experimental ΔΔG.sup.‡ with B3LYP+D3BJ, which are used to design new catalyst scaffolds.

An apparatus for generating a protein-drug interaction prediction model according to an aspect includes a data collection unit configured to collect protein data, drug molecular data, and interaction data between a protein and a drug molecule, a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data, and a model generation unit configured to train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data, and the interaction data as training data to generate a protein-drug interaction prediction model.

An apparatus for generating a protein-drug interaction prediction model according to an aspect includes a data collection unit configured to collect protein data, drug molecular data, and interaction data between a protein and a drug molecule, a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data, and a model generation unit configured to train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data, and the interaction data as training data to generate a protein-drug interaction prediction model.

A system can include one or more processors configured to identify a candidate molecule, provide the candidate molecule as an input to a simulation, operate the simulation, monitor at least one parameter of the simulation, modify the candidate molecule based on the at least one parameter, and output the modified candidate molecule responsive to a convergence condition being satisfied.

Computer-based methods that permit two or more users to perform simultaneous edits on a digitally encoded molecular structure. The methods use properties of conflict-free replicated data types (CRDT's) and causal trees to provide a distributed system which can manage the life-cycle of virtual molecular structures; including simultaneous editing, versioning, and provenance. Applications of the technology include, but are not limited to: simultaneous computer aided design of molecules in 2D or 3D in which users may be distributed across multiple computers and in which the need for computer time synchronization (offline or online editing) is obviated; version control and provenance tracking of a virtual molecule; and other types of data used in computer aided molecular design activities.

A system for prediction of clinical trial outcome. The system includes: a processor of a trial prediction (TP) node connected to at least one cloud server node over a network configured to host a machine learning (ML) module; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a clinical trial (CT) data, parse the CT data to derive drug molecules data, disease information data, and trial protocols data, encode the drug molecules data, the disease information data, and the trial protocols data into corresponding embeddings, generate knowledge pre-trained embeddings using external knowledge data, and provide the knowledge pre-trained embeddings to the ML module for prediction of the CT outcome.

A system for prediction of clinical trial outcome. The system includes: a processor of a trial prediction (TP) node connected to at least one cloud server node over a network configured to host a machine learning (ML) module; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a clinical trial (CT) data, parse the CT data to derive drug molecules data, disease information data, and trial protocols data, encode the drug molecules data, the disease information data, and the trial protocols data into corresponding embeddings, generate knowledge pre-trained embeddings using external knowledge data, and provide the knowledge pre-trained embeddings to the ML module for prediction of the CT outcome.