From advanced materials discovery to sustainable energy solutions, our computational approaches drive innovation across multiple domains.
Discovered novel allosteric binding sites through MD ensemble analysis, enabling a new class of selective inhibitors with improved drug-like properties and reduced off-target effects.
Discovered novel allosteric binding sites through MD ensemble analysis, leading to a 10-fold improvement in selectivity.
Applied extensive MD ensemble simulations to map protein conformational landscapes and identify cryptic allosteric pockets invisible to static crystal structures. Quantum mechanical scoring refined binding affinity predictions, while AI-guided optimization prioritized compounds with optimal selectivity profiles.
Identified optimal linker conformations using quantum molecular modeling, achieving a ternary complex geometry that maximized ubiquitination efficiency and degradation selectivity.
Identified optimal linker conformations using quantum molecular modeling, reducing experimental cycles by 60%.
Developed a quantum chemistry-guided framework for PROTAC linker optimization, systematically sampling linker length, rigidity, and chemical composition to identify conformations that stabilize the productive POI-PROTAC-E3 ternary complex. Integration of free energy calculations ensured thermodynamically favorable degrader designs.
Quantum-guided fragment linking for novel antimicrobial leads, identifying linker-payload combinations with optimal stability and cytotoxicity balance.
Quantum-guided fragment linking for novel antimicrobial leads achieved a DAR of 4 with superior stability profile.
Employed quantum mechanical calculations to predict linker hydrolysis rates and payload release profiles under physiological conditions. ADMET profiling of payload-linker combinations identified candidates with optimal therapeutic windows, reducing the experimental screening burden by prioritizing the most promising ADC designs.
Elucidated catalytic mechanism using QM/MM calculations and published in Nature Chemistry, providing the first atomistic description of metal-assisted substrate activation.
Full mechanistic characterization of a challenging metalloenzyme target, resulting in a Nature Chemistry publication and 3 patented inhibitor scaffolds.
Applied hybrid QM/MM methodology to model the reactive metal center with quantum mechanical accuracy while treating the protein environment at the molecular mechanics level. Transition state searches along the reaction coordinate revealed key mechanistic intermediates and provided a structural basis for rational inhibitor design targeting metal coordination.
Designed MOFs with tunable porosity and electronic structure using DFT and ML-guided screening for targeted applications in gas separation and catalysis.
Designed MOFs with tunable porosity and electronic structure using DFT and ML-guided screening, achieving 40% selectivity improvement over known materials.
Combined high-throughput DFT calculations of MOF electronic properties with machine learning surrogate models to screen thousands of hypothetical frameworks. Identified optimal linker-node combinations that simultaneously optimized pore geometry for target molecule selectivity and electronic structure for photocatalytic activity.
Improved ion transport and electrochemical stability through computational screening of MOF-derived electrode materials, targeting next-generation battery performance.
Improved ion transport and electrochemical stability through computational screening, yielding 25% capacity retention improvement after 500 cycles.
Performed DFT calculations of ion migration barriers and electronic conductivity in MOF-derived porous carbon architectures. Nudged elastic band calculations mapped optimal lithium diffusion pathways, while molecular dynamics simulations characterized electrolyte wetting behavior and interface stability under electrochemical cycling conditions.
Identified low-cost, high-activity hydrogen evolution catalysts by modeling adsorption energetics and reaction pathways, accelerating the transition to green hydrogen production.
Identified low-cost, high-activity hydrogen evolution catalysts with overpotential 80 mV lower than benchmark Pt catalysts.
Applied computational hydrogen electrode (CHE) methodology to screen hundreds of earth-abundant transition metal alloys and single-atom catalysts for hydrogen evolution activity. DFT-calculated adsorption free energies for H*, OH*, and O* intermediates predicted catalytic activity following Sabatier principle, with microkinetic modeling validating predicted turnover frequencies.
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