Physicochemical Prediction

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In fact, with the establishment of high-throughput analytical methodologies, the amount of physicochemical data is growing rapidly, allowing hundreds of compounds to be measured rapidly and precisely each day. The availability of large datasets covering a sufficiently broad structural diversity increases the chances that improved schemes for reasonably successful predictions of physicochemical properties can be developed. Both databases and chemoinformatics tools aim at enhancing the capacity of medicinal chemists to design compounds with desired physicochemical and pharmacological properties. Physicochemical prediction with computational approaches plays a crucial role in the drug-discovery research.

Application of Physicochemical Prediction
In research and drug discovery, the common values are pKa and octanol/water partition coefficients (logD and logP values) for physicochemical properties. For example, it is important not only to know the pKa value of an amine-containing compound, but even more so, to be able to modulate its basicity in a rational, structure-based fashion. Furthermore, the analysis of the relationship of logD and molecular weight (MW) with in vitro measures of compound metabolic stability and permeability concluded that the chances of achieving acceptable permeability and stability in a single molecule were highest when logD was between 1 and 2 and MW350-450. Therefore, study on physicochemical properties of drugs is very important in drug discovery.
Lead Sciences provides a full range of Physicochemical Prediction Services (include but not limited to the following):
Prediction of melting point
Melting point is an important property which can indicate whether a chemical compound is solid or liquid at particular temperatures. The melting point of a crystalline compound is controlled largely by intermolecular interactions and molecular symmetry. There are two approaches that have been used in the prediction of melting point-the physicochemical or structural descriptor approach and the group contribution approach.
Prediction of boiling point
Boiling point is an important property since it is an indicator of volatility, and can be used to predict vapour pressure. At Lead Sciences, many studies based on QSPR research for boiling point prediction have dealt with specific chemical classes and good correlations have generally been obtained.
Prediction of vapour pressure
The vapour pressure (VP) of a chemical controls its release into the atmosphere, and thus is an important factor in environmental distribution of chemicals. The variation of vapour pressure with temperature is given by the Clausius-Clapeyron equation: ln (VP2/VP1) =- (L/R)((1/T2) – (1/T1)) Where L = latent heat of vaporisation, and R = universal gas constant.
Prediction of aqueous solubility
Aqueous solubility depends not only on the affinity of a solute for water, but also on its affinity for its own crystal structure. We have created various ways in which aqueous solubilities can be predicted: in pure water, at a specified pH, at a specified ionic strength, as the intrinsic solubility undissociated species, or in the presence of other solvents or solutes.
Prediction of octanol-water partition coefficient (log Kow, log P)
The octanol-water partition coefficient is the ratio of concentrations of a chemical in n-octanol and in water at equilibrium at a specified temperature (typically 25oC). Partition coefficient is a good surrogate for partitioning of chemicals through lipid membranes, and is the most important physicochemical descriptor of biological activity.
Prediction of pKa
Within a congeneric series of chemicals, pKa is often closely correlated with the Hammett substituent constant, and this is the basis for a number of attempts at pKa prediction. There are a number of software programs that can predict multiple pKa values of organic chemicals.
Other properties
We can provide prediction services for other properties, such as density, refractive index, viscosity, surface tension, thermal conductivity and Henry’s law constant.
Our Services
Lead Sciences has the advanced analytical and computational technology. Besides, our researchers are always concerned about the development of drug physicochemical prediction field. Our experienced scientists will predict the physicochemical property such as the pKa and logD values of drugs with the appropriate methods.
References:
  1. Wildman, S. A., & Crippen, G. M. (1999). Prediction of physicochemical parameters by atomic contributions. Journal of chemical information and computer sciences, 39(5), 868-873.
  2. Taskinen, J., & Yliruusi, J. (2003). Prediction of physicochemical properties based on neural network modelling. Advanced drug delivery reviews, 55(9), 1163-1183.
  3. Morgenthaler, M., Schweizer, E., Hoffmann‐Röder, A., Benini, F., Martin, R. E., Jaeschke, G., & Schneider, J. (2007). Predicting and tuning physicochemical properties in lead optimization: amine basicities. ChemMedChem: Chemistry Enabling Drug Discovery, 2(8), 1100-1115.
  4. Waring, M. J. (2010). Lipophilicity in drug discovery. Expert Opinion on Drug Discovery, 5(3), 235-248.
  5. Edwards, M. P., & Price, D. A. (2010). Role of physicochemical properties and ligand lipophilicity efficiency in addressing drug safety risks. In Annual Reports in Medicinal Chemistry (Vol. 45, pp. 380-391). Academic Press.
Why are you working with us?
  • Industry-standard software and hardware
  • Proprietary design concepts and tools
  • An experienced scientist
  • A good track record of success
  • Close integration with related disciplines
  • Problem-solving expertise and creativity
  • Ability to execute patenting strategies
References
  1. Kapetanovic, I. M. (2008). Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chemico-biological interactions, 171(2), 165-176.
  2. Lu, P., Bevan, D. R., Leber, A., Hontecillas, R., Tubau-Juni, N., & Bassaganya-Riera, J. (2018). Computer-Aided Drug Discovery. Accelerated Path to Cures, 7-24.