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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Molecular Descriptors Property Prediction Using Transformer-Based Approach.

Tuan Tran1, Chinwe Ekenna1

  • 1Department of Computer Science, University at Albany, Albany, NY 12203, USA.

International Journal of Molecular Sciences
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces semi-supervised machine learning models for predicting molecular properties using SMILES strings. The approach achieves state-of-the-art performance, even with a novel 3D structure-based attention mechanism for drug discovery.

Keywords:
Plasmodium falciparumlarge-scale trainingmachine learningmolecular propertytransformers

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Area of Science:

  • Computational chemistry
  • Machine learning in drug discovery
  • cheminformatics

Background:

  • Predicting molecular properties is crucial for drug discovery.
  • Existing methods often require large labeled datasets.
  • Machine learning offers potential for efficient property prediction.

Purpose of the Study:

  • To develop and evaluate semi-supervised machine learning models for predicting molecular properties.
  • To leverage both labeled and unlabeled data for improved model training.
  • To explore efficient attention mechanisms for drug candidate prediction.

Main Methods:

  • A two-stage approach: pre-training using Masked Language Model on SMILES strings and fine-tuning on downstream tasks.
  • Utilizing large datasets of labeled and unlabeled SMILES strings.
  • Developing a novel 3D structure-based attention score for end-to-end transformer models.

Main Results:

  • The proposed semi-supervised models achieve performance comparable to state-of-the-art methods on MoleculeNet tasks.
  • The novel 3D structure-based attention approach enables comparable performance to pre-trained models with reduced computational cost.
  • The model successfully predicts anti-malaria drug candidates.

Conclusions:

  • Semi-supervised learning effectively predicts molecular properties and aids drug discovery.
  • Integrating 3D structural information offers computational efficiency without sacrificing performance.
  • The developed models represent a significant advancement in machine learning for cheminformatics.