Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.

  title={Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.},
  author={T{\^a}nia Cova and Carla Vitorino and M{\'a}rcio Ferreira and Sandra C. C. Nunes and Paola Rond{\'o}n-Villarreal and A. A. C. C. Pais},
  journal={Methods in molecular biology},
Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval… 



Towards the quantum-enabled technologies for development of drugs or delivery systems.

  • P. Hassanzadeh
  • Biology
    Journal of controlled release : official journal of the Controlled Release Society
  • 2020

Accelerating drug discovery

It is therefore timely to consider how new technologies, namely functional genomics, … create a growing problem both for the drug industry and for patients who are desperately waiting for new drugs to treat their illnesses.

Deep Learning-driven research for drug discovery: Tackling Malaria

The computational approach employing deep learning allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalaria target candidates.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing.

Deep learning and virtual drug screening

The broad basics and integration of both virtual screening (VS) and ML are explained and artificial neural networks (ANNs) are discussed and their usage for VS is discussed.

Principles of early drug discovery

This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development.

Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery

The DD approach utilizes quantitative structure–activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner.