Drug design using comparative molecular surface analysis

Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry

The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited.

Synthesis, antifeedant activity against Coleoptera and 3D QSAR study of alpha-asarone derivatives

For the first time, a set of 56 compounds representing structural derivatives of naturally occurring alpha-asarone as an antifeedants against stored product pests Sitophilus granarius L., Trogoderma granarium Ev., and Tribolium confusum Duv., were subjected to the 3D QSAR studies. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for 56 compounds, including 15 newly synthesized, were performed using comparative molecular field analysis s-CoMFA and SOM-CoMSA techniques. QSAR was conducted based on a combination of biological activity (against Coleoptera larvae and beetles) and various geometrical, topological, quantum-mechanical, electronic, and chromatographic descriptors. The CoMSA formalism coupled with IVE (CoMSA-IVE) allowed us to obtain highly predictive models for Trogoderma granarium Ev. larvae. We have found that this novel method indicates a clear molecular basis for activity and lipophilicity. This investigation will facilitate optimization of the design of new potential antifeedants.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.

Modeling robust QSAR 3: SOM-4D-QSAR with iterative variable elimination IVE-PLS: application to steroid, azo dye, and benzoic acid series

In the current paper we present a receptor-independent 4D-QSAR method based on self-organizing mapping (SOM-4D-QSAR) and in particular focus on its pharmacophore mapping ability. We use a novel stochastic procedure to verify the predictive ability of the method for a large population of 4D-QSAR models generated. This systematic study was conducted on a series of benzoic acids, azo dyes, and steroids that bind aromatase. We show that the 4D-QSAR method coupled with IVE-PLS provides a very stable and predictive modeling technique. The method enables us to identify the molecular motifs contributing the most to the fiber-dye affinity and the aromatase enzyme binding activity of the steroid. However, the method appeared much less effective for the benzoic acid series, in which the efficacy was limited by electronic effects strictly correlated to a single conformer.