Tu C. Le

Aqueous Solubility Prediction: Do Crystal Lattice Interactions Help?

Aqueous solubility is a very important physical property of small molecule drugs and drug candidates but also one of the most difficult to predict accurately. Aqueous solubility plays a major role in drug delivery and pharmacokinetics. It is believed that crystal lattice interactions are important in solubility and that including them in solubility models should improve the accuracy of the models. We used calculated values for lattice energy and sublimation enthalpy of organic molecules as descriptors to determine whether these would improve the accuracy of the aqueous solubility models. Multiple linear regression employing an expectation maximization algorithm and a sparse prior (MLREM) method and a nonlinear Bayesian regularized artificial neural network with a Laplacian prior (BRANNLP) were used to derive optimal predictive models of aqueous solubility of a large and highly diverse data set of 4558 organic compounds over a normal ambient temperature range of 20-30 °C (293-303 K). A randomly selected test set and compounds from a solubility challenge were used to estimate the predictive ability of the models. The BRANNLP method showed the best statistical results with squared correlation coefficients of 0.90 and standard errors of 0.645-0.665 log(S) for training and test sets. Surprisingly, including descriptors that captured crystal lattice interactions did not significantly improve the quality of these aqueous solubility models.

Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds

Accurate computational prediction of melting points and aqueous solubilities of organic compounds would be very useful but is notoriously difficult. Predicting the lattice energies of compounds is key to understanding and predicting their melting behavior and ultimately their solubility behavior. We report robust, predictive, quantitative structure-property relationship (QSPR) models for enthalpies of sublimation, crystal lattice energies, and melting points for a very large and structurally diverse set of small organic compounds. Sparse Bayesian feature selection and machine learning methods were employed to select the most relevant molecular descriptors for the model and to generate parsimonious quantitative models. The final enthalpy of sublimation model is a four-parameter multilinear equation that has an r(2) value of 0.96 and an average absolute error of 7.9 ± 0.3 kJ.mol(-1). The melting point model can predict this property with a standard error of 45° ± 1 K and r(2) value of 0.79. Given the size and diversity of the training data, these conceptually transparent and accurate models can be used to predict sublimation enthalpy, lattice energy, and melting points of organic compounds in general.

Discovery and optimization of materials using evolutionary approaches

Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.

An Experimental and Computational Approach to the Development of ZnO Nanoparticles that are Safe by Design.

Zinc oxide nanoparticles have found wide application due to their unique optoelectronic and photocatalytic characteristics. However, their safety aspects remain of critical concern, prompting the use of physicochemical modifications of pristine ZnO to reduce any potential toxicity. However, the relationships between these modifications and their effects on biology are complex and still relatively unexplored. To address this knowledge gap, a library of 45 types of ZnO nanoparticles with varying particle size, aspect ratio, doping type, doping concentration, and surface coating is synthesized, and their biological effects measured. Three biological assays measuring cell damage or stress are used to study the responses of human umbilical vein endothelial cells (HUVECs) or human hepatocellular liver carcinoma cells (HepG2) to the nanoparticles. These experimental data are used to develop quantitative and predictive computational models linking nanoparticle properties to cell viability, membrane integrity, and oxidative stress. It is found that the concentration of nanoparticles the cells are exposed to, the type of surface coating, the nature and extent of doping, and the aspect ratio of the particles make significant contributions to the cell toxicity of the nanoparticles tested. Our study shows that it is feasible to generate models that could be used to design or optimize nanoparticles with commercially useful properties that are also safe to humans and the environment.

A Bright Future for Evolutionary Methods in Drug Design

Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high‐throughput experimental methods allow exploration of drug‐like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery.

Predicting the complex phase behavior of self-assembling drug delivery nanoparticles

Amphiphilic lyotropic liquid crystalline self-assembled nanomaterials have important applications in the delivery of therapeutic and imaging agents. However, little is known about the effect of the incorporated drug on the structure of nanoparticles. Predicting these properties is widely considered intractable. We present computational models for three drug delivery carriers, loaded with 10 drugs at six concentrations and two temperatures. These models predicted phase behavior for 11 new drugs. Subsequent synchrotron small-angle X-ray scattering experiments validated the predictions.

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR

Neural networks have generated valuable Quantitative Structure‐Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so‐called “shallow” neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm‐shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome “activity cliffs” in QSAR data sets.

Manipulating the Ordered Nanostructure of Self-Assembled Monoolein and Phytantriol Nanoparticles with Unsaturated Fatty Acids

Mesophase structures of self-assembled lyotropic liquid crystalline nanoparticles are important factors that directly influence their ability to encapsulate and release drugs and their biological activities. However, it is difficult to predict and precisely control the mesophase behavior of these materials, especially in complex systems with several components. In this study, we report the controlled manipulation of mesophase structures of monoolein (MO) and phytantriol (PHYT) nanoparticles by adding unsaturated fatty acids (FAs). By using high throughput formulation and small-angle X-ray scattering characterization methods, the effects of FAs chain length, cis-trans isomerism, double bond location, and level of chain unsaturation on self-assembled systems are determined. Additionally, the influence of temperature on the phase behavior of these nanoparticles is analyzed. We found that in general, the addition of unsaturated FAs to MO and PHYT induces the formation of mesophases with higher Gaussian surface curvatures. As a result, a rich variety of lipid polymorphs are found to correspond with the increasing amounts of FAs. These phases include inverse bicontinuous cubic, inverse hexagonal, and discrete micellar cubic phases and microemulsion. However, there are substantial differences between the phase behavior of nanoparticles with trans FA, cis FAs with one double bond, and cis FAs with multiple double bonds. Therefore, the material library produced in this study will assist the selection and development of nanoparticle-based drug delivery systems with desired mesophase.

Modeling the Influence of Fatty Acid Incorporation on Mesophase Formation in Amphiphilic Therapeutic Delivery Systems

Dispersed amphiphile-fatty acid systems are of great interest in drug delivery and gene therapies because of their potential for triggered release of their payload. The mesophase behavior of these systems is extremely complex and is affected by environmental factors such as drug loading, percentage and nature of incorporated fatty acids, temperature, pH, and so forth. It is important to study phase behavior of amphiphilic materials as the mesophases directly influence the release rate of the incorporated drugs. We describe a robust machine learning method for predicting the phase behavior of these systems. We have developed models for each mesophase that simultaneous and reliably model the effects of amphiphile and fatty acid structure, concentration, and temperature and that make accurate predictions of these mesophases for conditions not used to train the models.

Illuminating Flash Point: Comprehensive Prediction Models

Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure – property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically diverse compounds, with flash point spanning from less than −130 °C to over 900 °C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials.