Conformer Search

Overview

Conformer search is a crucial task in computational chemistry, allowing researchers to explore the conformational space of molecules. This task is essential for understanding molecular behavior, interactions, and properties. It involves generating multiple conformers of a molecule, which are different spatial arrangements of the same molecular structure. These conformers can significantly influence the molecule’s properties and interactions with other molecules.

Our advanced conformational search engine employs state-of-the-art algorithms to efficiently discover stable molecular conformations while ensuring comprehensive coverage of the conformational landscape.

Methodology

Our conformational sampling utilizes the cutting-edge iMTD-GC (iterative Meta-Dynamics with Genetic Crossing) workflow, representing a significant advancement in conformer generation technology.

The iMTD-GC workflow combines the power of GFNN-xTB semi-empirical calculations with sophisticated root-mean-square-deviation (RMSD) based metadynamics simulations. This innovative approach systematically explores conformational space through enhanced sampling techniques while maintaining computational efficiency.

Workflow Architecture

┌──────────────────────────────┐
│       User Input             │
│  ──────────────────────────  │
│  Atomic coordinates          │
│  Charge and spin             │
│  Optional: xtb pre-optimized │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│    Workflow Initialization   │
│  ──────────────────────────  │
│  Sets MD params, biasing     │
│  factors, temperature, etc.  │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│  RMSD-based Metadynamics     │
│  ──────────────────────────  │
│  Multiple biased simulations │
│  explore conformational space│
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│  Structure Optimization      │
│  ──────────────────────────  │
│  Parallel optimization of    │
│  sampled geometries (ANCOPT) │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│  Ensemble Filtering & Sorting│
│  ──────────────────────────  │
│  Cluster, de-duplicate, and  │
│  apply energy cutoff (6 kcal)│
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│   Genetic Structure Crossing │
│  ──────────────────────────  │
│  Combines features of        │
│  selected low-energy states  │
└────────────┬─────────────────┘
             │
             ▼
┌──────────────────────────────┐
│     Conformer Ensemble Out   │
│  ──────────────────────────  │
│                              │
└──────────────────────────────┘

Input Configuration

The conformational search requires minimal user input, emphasizing ease of use and automation:

Input Molecule

Upload your input molecule in a supported format (e.g., XYZ) or you can give a SMILES string. The system will automatically generate the initial conformer. Check the Input and Visualizer section for more details.

Model

Select the computational model for the conformer search. Options include:

GFN1
GFN2
GFNFF

Solvent

Choose the solvent model for the conformer search. Options include:

Gas phase (Keep it None)
Implicit solvent (e.g., water)

Important

By default, only conformers within 6 kcal/mol above the lowest-energy conformer (as identified at the search level) are shown in output and can be used for further analysis.

Tip

It is usually wise to pre-optimize your input structure using xtb at the same theoretical level as intended for the conformational search. This increases the efficiency and quality of the generated conformers.

Advantages of the Module

Robust and General: No need to pre-define internal coordinates, making it applicable to a wide variety of molecular systems.
Bias-Free Sampling: Uses metadynamics based on RMSD to drive unbiased exploration of conformational space.
Efficiency: Efficiently locates low-lying conformers, reducing computational costs without compromising accuracy.
Safe and Reliable: Avoids pitfalls of earlier sampling algorithms with a more stable and validated workflow.
Extensible Design: Built for integration with downstream quantum chemistry or machine learning pipelines.

Important

A machine learning-based module for faster conformer generation is currently under development and will be available in a future release. Stay tuned for updates!