Our current project involves the development of a machine learning (ML) model tailored for predicting Absorption, Distribution, Metabolism, and Excretion (ADME) properties. Leveraging our in-house curated datasets comprising diverse chemical compounds with experimentally determined ADME profiles, we aim to train and validate robust ML algorithms. These models will integrate various molecular descriptors and structural features to accurately forecast ADME properties, facilitating the prioritization of drug candidates with improved pharmacokinetic profiles and reduced risk of toxicity during preclinical drug development stages.

Our project focuses on utilizing machine learning techniques to predict the solubility of chemical compounds, aiming to overcome challenges in lead molecule adsorption during drug discovery. By training ML models on comprehensive datasets of molecular structures and their corresponding solubility profiles, we aim to develop accurate predictive algorithms. These models will enable rapid assessment of solubility for new compounds, aiding in the identification of lead molecules with optimal absorption properties. Ultimately, our goal is to enhance the efficiency of drug discovery by prioritizing candidates with improved solubility, leading to more successful outcomes in the development of novel therapeutics.

Our project aims to leverage machine learning (ML) models to enhance drug delivery by focusing on Caco-2 permeability as a critical factor in drug adsorption. Caco-2 cells, mimicking the intestinal epithelium, serve as a valuable in vitro model for predicting a drug's ability to cross the intestinal barrier and enter systemic circulation. By integrating experimental data on Caco-2 permeability with advanced ML algorithms, we seek to develop predictive models capable of identifying compounds with enhanced intestinal absorption properties. This approach holds significant potential for streamlining drug development processes and optimizing drug delivery systems to improve the bioavailability and efficacy of pharmaceutical compounds.

Our project is focused on leveraging machine learning (ML) models to enhance drug delivery by targeting the Blood-Brain Barrier (BBB) in drug adsorption. The BBB serves as a formidable obstacle for drug molecules to penetrate into the brain, limiting the effectiveness of treatments for neurological disorders. By integrating experimental data on BBB permeability with advanced ML algorithms, we aim to develop predictive models capable of identifying compounds with enhanced ability to cross the BBB. This approach holds tremendous potential for optimizing drug formulations and delivery systems, ultimately improving the efficacy of treatments for various neurological conditions.

Our project involves the development of digital twins for skin, dental, and hair systems. A digital twin is a virtual representation of a physical entity, in this case, skin, dental structures, and hair follicles. These digital twins are created by integrating various data sources, including genetic, physiological, and environmental factors, to simulate the behavior and characteristics of the corresponding biological systems. By leveraging advanced computational models and machine learning algorithms, our goal is to generate accurate and personalized simulations that can be used for predictive analysis, treatment planning, and optimization of skincare, dental care, and hair care interventions. This innovative approach has the potential to revolutionize personalized healthcare by providing tailored solutions for individuals based on their unique biological characteristics and needs.

Our project focuses on developing a porous material adsorption module for various applications. Porous materials, such as metal-organic frameworks (MOFs) and porous polymers, possess high surface areas and tunable pore structures, making them excellent candidates for adsorption processes. Our module aims to leverage these materials for efficient adsorption of gases, liquids, and pollutants from air and water. By integrating advanced computational modeling and experimental validation, we aim to design and optimize porous material adsorbents tailored to specific adsorption targets. This project has the potential to contribute to environmental remediation, gas separation, and purification processes, offering sustainable solutions for diverse industrial and environmental challenges.

Our project employs computational techniques to optimize lipid nanoparticles for drug delivery applications. By simulating the interactions between lipid molecules and drug payloads, we aim to design nanoparticles with enhanced stability, loading capacity, and targeting specificity. Computational modeling allows us to predict the behavior of lipid nanoparticles in physiological conditions and optimize their formulation parameters to achieve desired drug release profiles. Through iterative simulations and experimental validation, we aim to accelerate the development of efficient lipid nanoparticle-based drug delivery systems, offering potential solutions for various therapeutic challenges.