Physicochemical Properties Prediction Service

Accurately predicting the physicochemical properties of molecules will provide reliable guidance for the analysis of lead compounds in the drug discovery process. As an AI-driven drug development service provider, CD ComputaBio uses advanced machine learning and deep learning algorithms to quickly and accurately predict the key physicochemical properties of compounds, including but not limited to solubility prediction, pK_a value calculation, LogP/LogD calculation, molecular stability prediction, crystal structure prediction, and many other contents.

Introduction to Drug's Physicochemical Properties

A drug's physicochemical properties are the inherent physical and chemical characteristics of a molecule that determine its behavior in biological systems and its suitability as a drug candidate. In drug development, understanding the physicochemical properties of a compound is a key step in understanding and modeling the compound's biological activity, pharmacokinetic properties, and potential toxicity. Traditional drug design workflows are often influenced by the experience of chemists and rely on time-consuming and expensive experiments to obtain relevant molecular properties. It takes an average of more than ten years and billions of dollars to bring a new drug to market.

Fig. 1 BSCA deep learning model architecture for molecular physicochemical property prediction. (Hou Y, et al.; 2022)

As a mainstream research tool, computer simulation methods have been widely studied for their potential to improve the efficiency and success rate of drug development, especially the prediction of molecular properties. By utilizing massive amounts of chemical data and advanced algorithms, researchers can predict the physical and chemical properties of compounds more quickly and accurately, accelerating the screening and optimization of candidate drugs.

Our Services

CD ComputaBio provides a full range of services for rapid prediction of physicochemical properties to meet the diverse needs of our customers. Our services cover the prediction of a wide range of molecular properties that are critical to drug design and optimization, including but not limited to the following:

Drug Stability	Using quantum chemical calculations and molecular dynamics (MD) simulations, the physicochemical stability of drugs under different environments is predicted, as well as potentially unstable functional groups and sites prone to degradation are identified. The quantitative structure-metabolism relationship (QSMR) model, molecular docking, and MD simulation are used to simulate the metabolism of drugs in the body, predict the metabolic rate, in vivo clearance rate, and the metabolic stability of drugs.
Drug Release Rate	Establish kinetic models of drug release, such as zero-order, first-order, Higuchi model, etc., to predict the release characteristics of different dosage forms, such as tablets, capsules, sustained/controlled-release preparations, etc. Applying machine learning algorithms to capture complex nonlinear relationships that are difficult to describe with traditional models from a large amount of experimental data, and achieve accurate prediction of biomacromolecules.
Solubility & Permeability	Using quantitative structure-activity relationship (QSAR) models, CD ComputaBio predicts the solubility of drugs in different solvents, pH and temperature conditions. Simulating the permeability of drugs through biological membranes, such as the intestinal epithelium and the blood-brain barrier, our scientists predict the passive diffusion and active transport capabilities of drugs.
Polymorphs & Crystal Structure	Our experts use computational crystallography to predict the various possible crystal forms of a drug and evaluate their thermodynamic and kinetic stability. We will also assist customers in studying the transition conditions and mechanisms between different crystal forms to prevent crystal form transitions during production and storage.
LogP/LogD &pKa	Using a large amount of data from known compounds to train the model, accurately predict the drug's partition coefficient LogP/LogD in the oil phase/water phase, and evaluate its lipophilicity or hydrophilicity. Predicting the drug's acid-base dissociation constant (pKa) and understanding its ionization state under different pH conditions.
Key Molecular Descriptors	The plan aims to use computational chemistry and quantum chemistry methods to accurately predict the physical and chemical properties of molecules, such as molecular weight, density, melting point, boiling point, etc., to provide key data support for customers' drug research and development. Modeling and analysis of compound characteristic parameters such as solvent accessible surface area (SASA), polar surface area (PSA), contact area analysis, and lipophilicity are also key components. This key information provides important support for drug design and screening.

Thanks to the best-in-class AI platform and high-performance computing resources, our experienced team of experts can handle the challenges of physicochemical properties prediction of various molecules. Our expertise includes but is not limited to:

Methods for Physicochemical Properties Prediction

Service Highlights

Thanks to high-performance computing resources, CD ComputaBio can handle any challenges related to the prediction of drug physicochemical properties on your drug development journey. Please don't hesitate to contact us, if you are interested in our services. Learn how we can help you accelerate your drug development process.

References:

1. Hou Y, et al. Accurate Physical Property Predictions via Deep Learning. Molecules. 2022; 27(5):1668.
2. Liu J, et al. Fragment-pair based drug molecule solubility prediction through attention mechanism. Front Pharmacol. 2023;14:1255181.

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