Quantum Chemistry Characterization

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The Problem You Are Trying to Solve

“I have lead compounds, and I want a deeper understanding of their molecular properties, such as metabolic stability, conformational behavior, electronic structure, and physicochemical properties, to guide confident lead optimization decisions.” At the lead optimization stage, activity alone is no longer sufficient. Teams often struggle with:

• Unclear metabolic liabilities or reactive hot spots
• Conformational flexibility that undermines binding or selectivity
• Poor solubility or formulation risk discovered too late
• Limited intuition for why small changes dramatically affect behavior

This workflow is designed to convert leads from black boxes into well-understood molecular systems, enabling rational optimization and risk reduction before expensive synthesis or in vivo work.

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Solution

This workflow leverages Revilico’s quantum chemistry and property prediction engines to provide a multi-layered, physics-informed understanding of lead molecules. The primary analysis chain is: Conformer Search → Geometry Minimization & Thermochemistry → Molecular Orbital Analysis → ADMET-AI & Solubility → Retrosynthesis Feasibility. Together, these steps explain:

• What shapes the molecule adopts, and the composition of these shapes in different contexts
• How stable and reactive it is electronically, and what is the most likely geometry it takes on
• How it is likely to behave in biological, metabolic, and formulation contexts
• Whether it is practical to make and scale

Integration with binding chemistry engines (Docking, MD, FEP) is possible before or after molecular-level risks or hypotheses are identified.

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What Data Do I Need to Provide?

Required

• Lead compound structures (SMILES, CSV, or SDF)

Recommended

• A small set of close analogs (for comparison and trend analysis)
• Any known experimental liabilities (clearance, solubility, toxicity flags)

Optional

• Target binding poses (to contextualize conformational or electronic findings)
• Proposed chemical modifications under consideration

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Workflow

1. Enumerate Realistic Molecular Conformations

Begin by understanding how the molecule behaves in 3D space. Use Conformer Search to:

• Generate a diverse ensemble of low-energy conformers
• Quantify flexibility, shape diversity, and Boltzmann populations
• Identify dominant conformations versus rare, strained geometries

This step answers:

• Is the molecule rigid or highly flexible?
• Does it naturally adopt binding-compatible shapes?
• Are there high-energy conformations likely to cause strain or instability?

This step gives a curated conformational ensemble with energies and population weights.

2. Optimize Geometry and Assess Thermodynamic Stability

Next, refine molecular structures at quantum-mechanical resolution. Use Geometry Minimization and Thermochemistry to:

• Optimize geometries using ab-initio / DFT methods along with Neural Network Potentials and Machine Learning Interatomic Potential models
• Compute electronic energy, enthalpy, and Gibbs free energy
• Detect unstable geometries or imaginary vibrational modes

This step helps determine:

• Intrinsic molecular stability
• Relative favorability of conformers or analogs
• Whether structural modifications introduce energetic penalties

This results in optimized 3D geometries and thermodynamic state functions.

3. Analyze Electronic Structure and Reactivity

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To understand reactivity, metabolism, and potential liabilities, examine electronic properties. Use Molecular Orbital (HOMO-LUMO) Analysis to:

• Compute HOMO/LUMO energies and gaps
• Visualize orbital distributions
• Assess electronic hotspots associated with reactivity or metabolism

This step informs:

• Likelihood of oxidative metabolism
• Potential covalent or reactive behavior
• Stability versus promiscuous reactivity

This results in orbital energies, gap analysis, and 3D electronic visualizations.

4. Evaluate Developability and Safety Risk

Now translate molecular features into biological risk. Use ADMET-AI to predict:

• Absorption and permeability
• Metabolic clearance and CYP interactions
• Toxicity risks (hERG, DILI, AMES, etc.)
• Key physicochemical properties (LogP, TPSA, RO5 compliance)

This step provides early answers to:

• Why might this lead fail in vivo?
• Which properties most urgently need improvement?
• Are risks structural or tunable?

This engine gives you confidence-scored ADMET profiles with interpretable alerts.

5. Assess Solubility and Formulation Risk

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Before committing to synthesis or scale-up, assess solubility behavior. Use Compound Solubility to:

• Predict solubility across solvents and temperatures
• Identify formulation-friendly conditions
• Flag solubility-driven bioavailability risk

This step is especially important when:

• Leads are lipophilic or aromatic-heavy
• Crystallization or formulation has been challenging historically

This step provides solubility curves and solvent-specific recommendations.

6. Evaluate Synthetic Feasibility

Finally, ensure your lead is practical to make. Use Retrosynthesis to:

• Propose ranked synthetic routes
• Identify starting material availability
• Highlight steps or motifs driving synthetic complexity

This step answers:

• Can this lead realistically be made and optimized?
• Which modifications are synthetically tractable?

This engine will output ranked retrosynthetic pathways with feasibility scores.

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Results

• A physics-grounded understanding of lead conformations, stability, and electronics
• Early identification of metabolic, solubility, and toxicity risks
• Clear structure–property relationships guiding next optimization steps
• Confidence that selected leads are not only active, but viable

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Now What? I understand my lead’s molecular behavior, but what’s next?

Typical next steps include:

• Targeted structural modifications informed by Revilico’s Quantum and ADMET insights
• Multi-parameter optimization using generative chemistry
• Binding refinement using docking, MD, or FEP
• Selecting candidates for synthesis and experimental validation

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Why Revilico?

Revilico enables deep lead characterization by integrating:

• Quantum chemistry for first-principles understanding
• AI property prediction for scalable risk assessment
• Conformational analysis for realistic 3D behavior
• Synthesis-aware planning to keep design grounded in reality

This allows teams to move into late-stage optimization with clarity, confidence, and fewer surprises.