AIMC Topic: Molecular Conformation

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Probing curcumin reactive conformers in keto-enol tautomerization enhanced by clustering with t-SNE.

Journal of molecular modeling
CONTEXT: The extensive conformational space of flexible molecules poses a significant challenge for predicting chemical reactivity through quantum chemical methods. For curcumin, whose keto-enol tautomerization is crucial to its biological activity a...

Enhancing Permeability Prediction of Heterobifunctional Degraders Using Machine Learning and Metadynamics-Informed 3D Molecular Descriptors.

Journal of chemical information and modeling
Heterobifunctional degraders, a class of targeted protein degraders (TPDs), often occupy beyond-rule-of-five (bRo5) chemical space, where traditional passive permeability models─calibrated on drug-like molecules or peptides and based on topological d...

Efficient Exploration of High-Dimensional Configuration Spaces for the Generation of Chemical Datasets.

Journal of chemical information and modeling
In this work, we introduce an automated methodology for the efficient and relatively inexpensive exploration of large high-dimensional chemical spaces, with particular focus on number-of-atoms-conserving processes, such as in mechanochemical reaction...

CSMILES: A Compact, Human-Readable SMILES Extension for Conformations.

Journal of chemical information and modeling
While line notation schemes for molecular structure are well developed, they are generally unable to distinguish different conformations of the same molecule. CSMILES, an extension to the ubiquitous line notation scheme, SMILES, has been developed to...

3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network.

Journal of chemical information and modeling
Existing methods for adsorption energy prediction primarily focus on individual molecules or static molecular pairs, lacking the capabilities to model the diverse spatial configurations found in complex solution systems. While traditional data sets a...

ConfRank+: Extending Conformer Ranking to Charged Molecules.

Journal of chemical information and modeling
We present a machine learning model for high-throughput energetic ranking of charged molecular conformers. Based on the ConfRank (Hölzer et al. , 8909-8925) approach, the model is trained in a pairwise fashion to predict energy differences for pair...

Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space.

Journal of chemical information and modeling
Machine learning (ML) has become a standard tool for the exploration of the chemical space. Much of the performance of such models depends on the chosen database for a given task. Here, this aspect is investigated for "chemical tasks" including the p...

Benchmarking 3D Structure-Based Molecule Generators.

Journal of chemical information and modeling
To understand the benefits and drawbacks of 3D combinatorial and deep learning generators, a novel benchmark was created focusing on the recreation of important protein-ligand interactions and 3D ligand conformations. Using the BindingMOAD data set w...

Combination of Molecular Dynamics Simulations and Machine Learning Reveals Structural Characteristics of Stereochemistry-Specific Interdigitation of Synthetic Monomycoloyl Glycerol Analogs.

Journal of chemical information and modeling
Synthetic monomycoloyl glycerol (MMG) analogs possess robust immunostimulatory activity and are investigated as adjuvants for subunit vaccines in preclinical and clinical studies. These synthetic lipids consist of a glycerol moiety attached to a cory...

DCGCN: Dual-Channel Graph Convolutional Network-Based Drug-Target Interaction Prediction Method with 3D Molecular Structure.

Journal of chemical information and modeling
Exploring drug-target interactions (DTIs) is crucial for drug discovery. Most existing methods for predicting DTIs rely solely on the linear structures of molecules, such as SMILES or the amino acid sequence. However, these linear features fail to re...