In a paper published in Current Opinion in Structural Biology, researchers discussed the expansion of the chemical space to libraries containing billions of synthesizable molecules, which opened opportunities for drug discovery but also challenged the power of computer-aided drug design to prioritize the best candidates.
It directly impacted quantum mechanics (QM) methods, which provided chemically accurate properties but were limited to small-sized systems.
Study: Adapting Quantum Methods for Modern Drug Discovery. Image Credit: metamorworks/Shutterstock.com
Preserving accuracy while optimizing computational cost was central to developing high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning (ML), combined with supercomputing resources, enhanced the ability to use these methods in drug discovery.
Related Work
Electronic structure methods provided a robust framework to explore chemical reactivity in biomolecular systems. The QM cluster and QM/molecular mechanics (MM) approaches disclosed factors underlying enzyme catalytic efficiency and were applied to problems like designing covalent inhibitors and activating prodrugs.
Despite early computational cost limitations, algorithm advancements, including ML integration and improved computer performance, enabled QM-based strategies in drug design.
Ligand Sampling
Conformational sampling of ligands is crucial for characterizing physicochemical properties, defining pharmacophore models, and predicting solubility and permeability. Despite improvements in force fields, MM methods often need to improve accuracy for novel chemical motifs, making QM methods more suitable.
Advances like the extended force field (XFF) force field and systematic QM conformational analysis have addressed these challenges. Tools such as the conformer-rotamer ensemble sampling tool (CREST) program and the auto3D package, which utilize QM and ML techniques, have significantly improved the exploration of conformational space.
Studies have demonstrated the efficacy of these methods in predicting binding-free energies and torsional energetics with high accuracy.
Medicinal Chemistry Refinement
Refinement of the experimental binding pose is critical in medicinal chemistry studies to ensure accurate ligand placement within the protein binding pocket. QM and QM/ (QM/MM) methods excel by refining ligand geometry under the protein's electrostatic field, improving internal parameters and intermolecular distances.
Notably, these methods discriminated between potential binding poses of the O-aryl carbamate inhibitor and elucidated mechanisms leading to covalent adduct formation in different studies.
These methods enhance traditional crystallographic refinement by incorporating QM/MM schemes that optimize ligand geometry based on experimental data. Recent advancements include QM restraints (QMR), which maximize ligand positioning within the protein environment more accurately than conventional methods.
Automated fragmentation QM/MM (AF-QM/MM) enables efficient structure prediction using nuclear magnetic resonance (NMR) data, offering insights into ligand binding dynamics and chemical shift perturbations induced by protein interactions. Moreover, QM/MM molecular dynamics ensembles refine initial crystal structures, aligning more closely with experimental data and highlighting discrepancies that single structures may overlook.
Advancing Drug Discovery
Accurate binding free energy calculation remains a significant challenge in drug discovery and is crucial for guiding structure-based drug design efforts. QM methods, ranging from semiempirical calculations to rigorous coupled-cluster approaches, have been pivotal in this domain.
Recent advancements include developing QM-based scoring functions and applying the 'divide-and-conquer' fragment molecular orbital (FMO) method in sophosQM. These approaches integrate QM calculations to enhance accuracy by considering interaction energies and non-enthalpic contributions, improving predictions of binding affinity dynamics influenced by protein interactions.
Addressing computational costs associated with QM methods has spurred innovations in force field development and ML applications. The ARROW force field exemplifies advanced physics-based models incorporating multipolar electrostatics and anisotropic polarization calibrated against high-level QM data.
ML techniques like chemprop also leverage curated datasets such as QM Properties of drug-like molecules (Q-Mugs) and QM9-extended to highly accurately predict molecular properties like octanol/water partition coefficients. These approaches represent a shift towards more efficient and predictive models in drug discovery, balancing computational feasibility with enhanced predictive power.
Understanding the structural and chemical attributes influencing ligand bioactivity is essential for rational drug design. QM methods excel in analyzing specific interactions like halogen bonds and polarized interactions that MM force fields struggle to capture accurately.
Applications extend to bioisosteric replacements and similarity-based screening strategies, enhancing the scope of structure-activity relationship studies and pharmacophore-based design. Integration of QM-derived descriptors in virtual screening campaigns and protein-protein interaction studies underscores their pivotal role in uncovering key molecular interactions and optimizing drug-like properties.
Conclusion
To sum up, the success of QM-based strategies in drug design was determined by advancements in algorithms and hardware. Efforts focused on refining semiempirical methods, enhancing QM/MM approaches, and developing QM-tailored force fields and ML models.
Novel supercomputing architectures and the potential of quantum computing paved the way for significant progress in quantum chemistry simulations, marking an exciting evolution in drug discovery methodologies.
Journal Reference
Ginex, T. et al. (2024). Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design. Current Opinion in Structural Biology, 87, 102870. doi: 10.1016/j.sbi.2024.102870. https://www.sciencedirect.com/science/article/pii/S0959440X24000976
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