The Next Era: Deep Learning in Pharmaceutical Research

Abstract

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Advancing drug discovery via GPU-based deep learning

“…Advanced GPU technology may in fact be game-changing in this regard, not only by leading to the necessary massive computational speedup, but by enabling us to perform studies that have previously been formulated merely as Gedankenexperiments. For the current applications of GPU-based DNNs in drug discovery we agree with Ekins [21] in that more prospective testing is needed to evaluate the actual usefulness of this technology for the field. If DNNs can lead to encapsulation of molecular representations which correlate with heterogeneous, multi-criteria pharmaceutical data and can be subsequently applied for multi-objective design (whether machine-driven or manual), then DNN-driven virtual screening may become a formidable tool for accelerating drug discovery efforts.…”

Automated discovery of GPCR bioactive ligands

“…In recent years, machine learning has become one of the most widely used approaches in drug discovery and development [31, [41][42][43][44][45][46][47]. Often, machine learning is combined with structure-based, ligand-based, and high-throughput screening to automate QSAR-based target prioritization in iterative and automated or semi-automated virtual screening pipelines ( Figure 2).…”

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

“…In recent times, deep learning has permeated almost every aspect of computational science, including Bioinformatics and Drug discovery [43][44][45]. Our current work is motivated by the success of deep matrix factorization [46][47][48] and deep dictionary learning [49].…”