Conformer ensemble energy ranking

conformer-ensemble-energy-ranking

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Summary

Rank and filter conformer ensembles generated by RDKit using neural-network potential energies computed by ASE-ANI, retaining only the lowest-energy subset for downstream quantum-chemical calculations. This step reduces computational cost and focuses conformational sampling on thermodynamically favorable structures.

When to use

After RDKit has generated a large set of 3D conformers for a molecule in SDF or XYZ format, and before submitting conformers to computationally expensive quantum-chemical methods (e.g., QUICK). Use this skill when you need to pre-screen conformers to avoid wasting HPC resources on high-energy, non-viable geometries.

When NOT to use

• Input conformers are already filtered by a more rigorous method (e.g., molecular dynamics or explicit solvent sampling); re-filtering adds no value.
• The molecule contains elements outside the CHNO set supported by ANI-1x/ANI-1ccx (e.g., halogens, transition metals); ASE-ANI will fail or produce undefined predictions.
• Computational budget or HPC access permits direct quantum-chemical screening of all conformers; energy pre-filtering is unnecessary overhead.

Inputs

• Conformer ensemble in SDF or XYZ format (output from RDKit conformation generation)
• ASE-ANI potential (ANI-1x or ANI-1ccx neural network model)
• Selection parameters: number of conformers to retain (N) or energy threshold (ΔE in kcal/mol)

Outputs

• Filtered conformer ensemble (XYZ or compatible format)
• Energy-ranked conformer list (mapping conformer ID to computed energy in eV)
• Conformer subset metadata (indices, energies, selection criterion applied)

How to apply

Load all RDKit-generated conformer structures (typically in SDF or XYZ format) into ASE-ANI. For each conformer, compute a single-point energy using the ANI-1x or ANI-1ccx neural-network potential, which provides DFT-quality predictions at force-field speed. Rank all conformers by their computed energy and apply one of two selection criteria: (1) retain the top N lowest-energy conformers (e.g., top 5–10), or (2) apply an energy threshold (e.g., retain all conformers within 5–10 kcal/mol of the global minimum). Export the filtered subset in a format compatible with downstream quantum-chemical software (e.g., XYZ for QUICK). The rationale is that ASE-ANI rapidly eliminates implausible conformations while preserving thermodynamically accessible states for Boltzmann-weighted CCS ensemble averaging.

Related tools

• RDKit (Generates initial conformer ensemble; conformer-ensemble-energy-ranking filters its output) — https://www.rdkit.org
• ASE-ANI (Computes single-point neural-network potential energies for each conformer and enables ranking) — https://github.com/isayev/ASE_ANI
• QUICK (Downstream quantum-chemical tool that receives the filtered conformer subset for CCS calculation) — https://github.com/merzlab/QUICK
• Snakemake (Orchestrates the conformer generation, filtering, and quantum-calculation workflow with parallelization on HPC systems) — https://github.com/DasSusanta/snakemake_ccs

Examples

python -c "from ase import Atoms; from ase_ani import ANI; import ase.io; structures = ase.io.read('conformers.sdf', index=':'); calc = ANI(); energies = []; [energies.append(s.calc_potential_energy(calc)) for s in structures]; top_n = sorted(zip(energies, range(len(energies))), key=lambda x: x[0])[:5]; [ase.io.write(f'filtered_{i}.xyz', structures[idx]) for e, idx in top_n]"

Evaluation signals

• Energy values are computed and differ across the conformer ensemble (no single energy for all conformers; indicates ASE-ANI ran successfully).
• Filtered conformer count is either exactly N (if N-conformer selection) or all conformers fall within the specified energy threshold ΔE of the minimum.
• Output geometry files are valid XYZ or SDF with no structural corruption (parseable by downstream tools).
• Ensemble-averaged CCS values from the filtered conformers are stable and reproducible across re-filtering runs (convergence test).
• Energy ranking is monotonically increasing or flat; no out-of-order conformers in the ranked list.

Limitations

• ASE-ANI is deprecated and no longer actively supported; users should transition to TorchANI (PyTorch implementation). The original ASE-ANI codebase requires Python 3.6, CUDA 9.2, and modern NVIDIA GPUs (compute capability ≥5.0) and works only on Ubuntu Linux variants.
• ASE-ANI potentials (ANI-1x, ANI-1ccx) support only CHNO elements; molecules containing halogens, phosphorus, sulfur, or transition metals will fail or produce unreliable predictions.
• Neural-network energies are approximate and trained on gas-phase geometries; they do not account for solvation, counterions, or pH effects relevant to experimental ESI-MS conditions. Post-filtering by quantum methods is essential for accuracy.
• Selection threshold (N or ΔE) is user-defined and influences downstream CCS statistics; no data-driven guidance is provided for optimal threshold choice. Over-filtering (small N) may miss conformers important for Boltzmann averaging; under-filtering (large N or ΔE) negates computational savings.

Evidence

• [other] Load conformer structures generated by RDKit in a standard molecular format (e.g., SDF or XYZ). Initialize ASE-ANI potential and compute single-point energy for each conformer. Rank conformers by energy and select the lowest-energy subset (retain top N conformers or apply an energy threshold). Export filtered conformer set in a format compatible with downstream quantum-chemical software (QUICK).: "Load conformer structures generated by RDKit in a standard molecular format (e.g., SDF or XYZ). Initialize ASE-ANI potential and compute single-point energy for each conformer. Rank conformers by"
• [other] The workflow uses ASE-ANI for conformation filtering as a step following RDKit conformation generation, operating as part of the automated pipeline for CCS predictions.: "The workflow uses ASE-ANI for conformation filtering as a step following RDKit conformation generation, operating as part of the automated pipeline for CCS predictions."
• [readme] DEPRECATED and no longer supported, please use TorchANI implementation: "DEPRECATED and no longer supported, please use TorchANI implementation"
• [readme] Current ANI-1x and ANI-1ccx potentials provide predictions for the CHNO elements.: "Current ANI-1x and ANI-1ccx potentials provide predictions for the CHNO elements."
• [readme] NOTICE: Python binaries built for python 3.6 and CUDA 9.2. Works only under Ubuntu variants of Linux with a NVIDIA GPU: "NOTICE: Python binaries built for python 3.6 and CUDA 9.2. Works only under Ubuntu variants of Linux with a NVIDIA GPU"
• [readme] workflow allows users to predict CCS values for multiple protonated/deprotonated adducts and models with high automation and parallelized computation on high-performance computing (HPC) systems: "workflow allows users to predict CCS values for multiple protonated/deprotonated adducts and models with high automation and parallelized computation on high-performance computing (HPC) systems"