Three-Dimensional Analysis of Binding Sites for Predicting Binding Affinities in Drug Design

Development of a predictive model for gravimetric powder feeding from an API-rich materials properties library

A model was developed for predicting the feed factor profile of a powder, processed through a gravimetric feeder, as a function of material properties and process parameters. Predictive models proposed in existing literature have often used excipients and active pharmaceutical ingredients (APIs) with good powder flow characteristics in their development. In this work, a material properties library containing a large proportion of APIs, as well as excipients and co-processed blends, was used to build the model and enhance the prediction of feed factor profile for cohesive powders. Gravimetric feeder trials were performed at varying mass flow rates and screw geometries to determine the feed factor profiles. A semi-empirical exponential model, with parameters f_max, f_min, and β, was then used to fit the experimental feed factor profiles. Bayesian optimisation and Support Vector Regression (SVR) modelling techniques were utilised to optimise and predict the exponential model parameters as a function of material properties. The parameters found to strongly influence the model were particle size, bulk density, FFC and FT4 rheometer parameters. Results showed low prediction errors between the estimated and experimental data. The final model produces good estimations of the feed factor profile and requires minimal powder consumption.

Artificial intelligence in drug design: algorithms, applications, challenges and ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

Deep Learning in Virtual Screening: Recent Applications and Developments

Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied in the context of computer-aided drug discovery. Recently, thanks to the rise of novel technologies as well as the increasing amount of available chemical and bioactivity data, deep learning has gained a tremendous impact in rational active compound discovery. Herein, recent applications and developments of machine learning, with a focus on deep learning, in virtual screening for active compound design are reviewed. This includes introducing different compound and protein encodings, deep learning techniques as well as frequently used bioactivity and benchmark data sets for model training and testing. Finally, the present state-of-the-art, including the current challenges and emerging problems, are examined and discussed.