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Geometric Configuration Prediction


The geometry or conformation of a molecule is frequently referred to as the three-dimensional (3D) coordinates of its atoms. Conformational generation is the process of forecasting a molecule's potential effective coordinates, which is crucial for figuring out a molecule's chemical and physical characteristics. Applications include the creation of 3D quantitative conformational relationships (QSAR), structure-based virtual screening, and pharmacophore modeling all heavily rely on molecular conformation generation.

Conformations can often be determined in a physical environment using instrumental techniques such as X-ray crystallography, as well as using experimental techniques. However, these methods are often time consuming and costly. For this reason, Alfa Chemistry offers its customers a molecular geometry conformation prediction service, using a number of computational methods for conformation generation to assist them in their applications.

Our Methods

Method 1: Conventional molecular force field approach

Alfa Chemistry resolves this issue by first calculating the energy of a molecule using a force field energy function, and then minimizing that energy with relation to the molecule's coordinates.

Based on the atoms, bonds, and coordinates of the molecule, this energy function calculates an approximation of the true potential energy of the molecule as it is seen in nature. The molecule's most stable conformation is represented by the minimum of this energy function.

It has been demonstrated that the molecular force field energy function is typically a rough approximation of the actual molecular energy, despite the fact that this method is typically used to generate geometrically distinct sets of conformations, some of which resemble the lowest energy conformation.

Conventional molecular force field approach

Method 2: Deep generative graph neural network

We employ a deep generative graph neural network that generates molecular conformations that are energetically advantageous and more likely to be observed experimentally. This network learns the energy function from the data in an end-to-end manner. This is accomplished by maximizing the likelihood of molecular reference conformations in the dataset.

The root-mean-square deviation (RMSD) between the generated conformation and the reference conformation is lower in the conformations created by this model than in the conformations obtained by conventional force field approaches. Despite having a smaller variance, the approach does not yield conformations that are geometrically comparable. Additionally, this approach performs computations more quickly than the force field approach.

Our Service

Project NameMolecular Geometric Configuration Prediction Service
DeliverablesWe provide all raw data and analysis services to our customers.
Samples RequirementOur services require specific requirements from you.
Timeline DecideAccording to customers' needs
PricePlease contact us for an inquiry

Alfa Chemistry can also provide you with but not limited to:

This figure shows the three molecules in each dataset whose RMSD decreased the most and the three whose RMSD increased the most on applying MMFF to the conformations predicted by the neural network.Fig 1. This figure shows the three molecules in each dataset whose RMSD decreased the most and the three whose RMSD increased the most on applying MMFF to the conformations predicted by the neural network. (Mansimov E, et al. 2019)

Alfa Chemistry offers clients globally quick, competent, high-quality molecular geometric configuration prediction services at reasonable pricing, which can lower the expense of post-experiments. A unique, specialized, and tailored research service is the molecular geometric configuration prediction service. Each project needs to be assessed before the best pricing and analysis approach are chosen.

Customers can speak with our team directly and receive prompt responses to their questions. Please get in touch with us if you're interested in learning more about our services.


  • Mansimov E, et al. (2019). "Molecular Geometry Prediction using a Deep Generative Graph Neural Network." Scientific Reports. 9: 20381.

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