A5011287493- Computational Drug Discovery Methods
- Machine Learning in Materials Science
- Protein Structure and Dynamics
- Advanced Polymer Synthesis and Characterization
- Cancer therapeutics and mechanisms
- RNA and protein synthesis mechanisms
- Chemical Reactions and Isotopes
- Innovative Microfluidic and Catalytic Techniques Innovation
- Various Chemistry Research Topics
- DNA and Nucleic Acid Chemistry
- vaccines and immunoinformatics approaches
- Marine Invertebrate Physiology and Ecology
- Advanced Sensor and Energy Harvesting Materials
- SARS-CoV-2 and COVID-19 Research
- Mechanisms of cancer metastasis
- Advanced Proteomics Techniques and Applications
- Marine and environmental studies
- Melanoma and MAPK Pathways
- Conducting polymers and applications
- Metal complexes synthesis and properties
- Bacterial Genetics and Biotechnology
- Synthesis and biological activity
- Water Quality Monitoring and Analysis
- Coral and Marine Ecosystems Studies
- Genomics and Chromatin Dynamics
Carnegie Mellon University
2021-2025
Lomonosov Moscow State University
2015
Modern polymer science suffers from the curse of multidimensionality. The large chemical space imposed by including combinations monomers into a statistical copolymer overwhelms synthesis and characterization technology limits ability to systematically study structure–property relationships. To tackle this challenge in context 19F magnetic resonance imaging (MRI) agents, we pursued computer-guided materials discovery approach that combines synergistic innovations automated flow machine...
In silico identification of potent protein inhibitors commonly requires prediction a ligand binding free energy (BFE). Thermodynamics integration (TI) based on molecular dynamics (MD) simulations is BFE calculation method capable acquiring accurate BFE, but it computationally expensive and time-consuming. this work, we have developed an efficient automated workflow for identifying compounds with the lowest among thousands congeneric ligands, which only hundreds TI calculations. Automated...
In this work, we combined Deep Docking and free energy MD simulations for the in silico screening experimental validation potential inhibitors of leucine rich repeat kinase 2 (LRRK2) targeting WD40 (WDR) domain.
The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field computational hit-finding. Here we report results inaugural challenge which 23 teams each selected up 100 commercially available compounds that they predicted would bind WDR domain Parkinson's disease target LRRK2, with no known ligand and only an apo structure PDB. lack binding data presumably low druggability is hit finding methods. Of 1955 molecules by participants Round 1 challenge,...
The COVID-19 pandemic continues to pose a substantial threat human lives and is likely do so for years come. Despite the availability of vaccines, searching efficient small-molecule drugs that are widely available, including in low- middle-income countries, an ongoing challenge. In this work, we report results open science community effort, "Billion molecules against challenge", identify inhibitors SARS-CoV-2 or relevant receptors. Participating teams used wide variety computational methods...
The development of high-performance elastomers for additive manufacturing requires overcoming complex property trade-offs that challenge conventional material discovery pipelines. Here, a human-in-the-loop reinforcement learning (RL) approach is used to discover exceptional polyurethane overcome pervasive stress–strain tradeoffs. Starting with diverse training set 92 formulations, coupled multi-component reward system was identified guides RL agents toward materials both high strength and...
This review examines multifunctional X-ray systems of the new generation and their role in expanding diagnostic capabilities regional medical institutions. It is shown that implementation complexes contributes to early detection respiratory musculoskeletal pathologies, expands possibilities for minimally invasive interventions under visual control, increases availability high-tech diagnostics resource-limited settings. The economic clinical effectiveness universal overcoming healthcare...
Proteochemometric models (PCM) are used in computational drug discovery to leverage both protein and ligand representations for bioactivity prediction. While machine learning (ML) deep (DL) have come dominate PCMs, often serving as scoring functions, rigorous evaluation standards not always been consistently applied. In this study, using kinase-ligand prediction a model system, we highlight the critical roles of dataset curation, permutation testing, class imbalances, data splitting...
Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility. However, maintaining quality control without constant human oversight remains a critical challenge. Here, we present novel machine learning framework for automated anomaly detection High-Performance Liquid Chromatography (HPLC) conducted lab. Our system specifically targets air bubble contamination—a common yet challenging issue that...
The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each which followed by experimental validation the predictions.. For CACHE #1, Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 (WDR) domain was selected as target for in silico optimization. Mutations LRRK2 are most common genetic cause...
ABSTRACT The CACHE challenges are a series of prospective benchmarking exercises meant to evaluate progress in the field computational hit-finding. Here we report results inaugural #1 challenge which 23 teams each selected up 100 commercially available compounds that they predicted would bind WDR domain Parkinson’s disease target LRRK2, with no known ligand and only an apo structure PDB. lack binding data presumably low druggability is hit finding methods. Seventy-three 1955 procured...
Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison their amorphous counterparts. Certain molecular thin films can be transitioned from initially prepared layers large-scale crystalline via abrupt thermal annealing. Ideally, these crystallize as platelets with long-range-ordered domains on the scale of tens hundreds microns. However, other may instead spherulites or resist crystallization entirely. Organic molecules...
The leucine-rich repeat kinase 2 (LRRK2) is the most mutated gene in familial Parkinson’s disease, whose mutations lead to pathogenic hallmarks of disease. LRRK2 WDR domain an understudied drug target for disease with no known inhibitors prior first phase Critical Assessment Computational Hit-Finding Experiments (CACHE) Challenge. CACHE challenges are designed attract state-of-the-art computational methods both hit-finding and optimization small molecule challenging protein targets. A unique...
In silico identification of potent protein inhibitors commonly requires prediction a ligand binding free energy (BFE). Thermodynamics integration (TI) based on molecular dynamics (MD) simulations is BFE calculation method capable predicting accurate BFE, but it computationally expensive and time-consuming. this work, we developed an efficient automated workflow for identifying compounds with the lowest among thousands congeneric ligands which only hundreds TI calculations. Automated Machine...
Modern polymer science is plagued by the curse of multidimensionality; large chemical space imposed including combinations monomers into a statistical copolymer overwhelms synthesis and characterization technology limits ability to systematically study structure–property relationships. To tackle this challenge in context 19F MRI agents, we pursued computer-guided materials discovery approach that combines synergistic innovations automated flow machine learning (ML) method development. A...
Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison their amorphous counterparts. Certain molecular thin films can be transitioned from initially prepared layers large-scale crystalline via abrupt thermal annealing. Ideally, these crystallize as platelets with long-range-ordered domains on the scale of tens hundreds microns. However, other may instead spherulites or resist crystallization entirely. Organic molecules...
Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison their amorphous counterparts. Certain molecular thin films can be transitioned from initially prepared layers large-scale crystalline via abrupt thermal annealing. Ideally, these crystallize as platelets with long-range-ordered domains on the scale of tens hundreds microns. However, other may instead spherulites or resist crystallization entirely. Organic molecules...
The COVID-19 pandemic continues to pose a substantial threat human lives and is likely do so for years come. Despite the availability of vaccines, searching efficient small-molecule drugs that are widely available, including in low- middle-income countries, an ongoing challenge. In this work, we report results community effort, “Billion molecules against Covid-19 challenge”, identify inhibitors SARS-CoV-2 or relevant receptors. Participating teams used wide variety computational methods...
The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge is focused on identifying small molecule inhibitors protein targets using computational methods. For the CACHE #1, Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 (WDR) domain was selected as target for in silico hit-finding and optimization. Mutations LRRK2 are most common genetic cause familial form Parkinson's disease. WDR an understudied drug with no known molecular inhibitors. We developed a framework...