AIMC Topic: Molecular Structure

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Naturally occurring caffeic acid phenethyl ester from chestnut honey-based propolis and virtual screening towards DYRK1A.

Natural product research
Neurodegenerative diseases (NDDs) are disorders with dysfunction and ongoing loss of neurons, glial cells and the neural networks in the brain and spinal cord. DYRK1A protein was reported to modulate to the cytoskeletal fraction in human and mouse br...

The emergence of machine learning force fields in drug design.

Medicinal research reviews
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, ma...

Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A receptor antagonists.

Journal of biomolecular structure & dynamics
The Adenosine A receptor (AAR) is considered a novel potential target for the immunotherapy of cancer, and AAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of ben...

1,3,4-oxadiazole derivatives: synthesis, characterization, antifungal activity, DNA binding investigations, TD-DFT calculations, and molecular modelling.

Journal of biomolecular structure & dynamics
1,3,4-Oxadiazole-based heterocyclic analogs (3a-3m) were synthesized cyclization of Schiff bases with substituted aldehydes in the presence of bromine and acetic acid. The structural clarification of synthesized molecules was carried out with variou...

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules.

Journal of chemical information and modeling
We introduce an exploratory active learning (AL) algorithm using Gaussian process regression and marginalized graph kernel (GPR-MGK) to sample chemical compound space (CCS) at minimal cost. Targeting 251,728 enumerated alkane molecules with 4-19 carb...

Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information.

Journal of computer-aided molecular design
In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty...

Graph neural networks for the identification of novel inhibitors of a small RNA.

SLAS discovery : advancing life sciences R & D
MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzin...

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Journal of biomolecular structure & dynamics
Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined o...

DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening.

Journal of chemical information and modeling
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address...

Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training.

Journal of chemical information and modeling
A key aspect of producing accurate and reliable machine learning models for the prediction of properties of quantum chemistry (QC) data is identifying possible data characteristics that may negatively influence model training. In previous work, we id...