Future Medicinal Chemistry, 13 Aug 2018
Artificial intelligence in drug discovery
Matthew A Sellwood, Mohamed Ahmed, Marwin HS Segler & Nathan Brown
There has been a great deal of hype surrounding the resurgence of Artificial Intelligence and Machine Learning. This commentary was published in Future Medicinal Chemistry as a brief overview of the AI and ML domains, their relevance in different aspects of drug discovery and, importantly, reflecting on managing expectations from different quarters. The key themes covered are molecular design approaches, including our recent paper on do novo design models, predictive modelling, synthesis planning, and closing the feedback loop to learn from our decisions.
british medical journal, 7 june 2018
Clinical trial design and dissemination: comprehensive analysis of clinicaltrials.gov and PubMed data since 2005
Clinicaltrials.gov is the world’s largest primary registry of clinical studies. For almost two decades now it has been helping physicians, patients, and regulators identify relevant trials and collect evidence. It also offers a unique opportunity to explore, examine, and monitor the clinical research landscape. In our recent research paper, we used the clinicaltrials.gov registry data to conduct a comprehensive large-scale analysis of registered clinical trials and investigate trends in their design and transparency.
Progress in Medicinal Chemistry, Volume 57, elsevier, 10 April 2018
Chapter Five - Big Data in Drug Discovery
Nathan Brown, Jean Cambruzzi, Peter J. Cox, Mark Davies, James Dunbar, Dean Plumbley, Matthew A.Sellwood, Aaron Sim, Bryn I. Williams-Jones, Magdalena Zwierzyna, David W.Sheppard
Modern scientific discovery is driven by data and learning from those data. This book chapter offers an overview of available data sources of relevance to drug discovery and how these can and do make an impact in our research and predictions to make better informed decisions that more rapidly make changes in our discovery research ethic to progress drugs to the clinic.
Nature Chemistry, 4 April 2018
Organic synthesis provides opportunities to transform drug discovery
Ian Churcher et al
Ian Churcher, VP Drug Discovery recently published a paper in Nature to highlight how organic synthesis could represent an opportunity for the pharmaceuticals industries to improve drug development. He presents the current challenges that the industry needs overcome and explains how new technologies and industry-academia collaborations are essential to progress.
Nature, 28 March 2018
Planning chemical syntheses with deep neural networks and symbolic AI
Marwin Segler et al
The AI technology developed by Marwin uses deep neural networks to learn from every chemical reaction ever performed (12.4 million of them). Combined with modern tree search algorithms, this allows to plan the synthesis of novel molecules. The technology augments the ability of chemists to make molecules faster, increases the success rate of synthetic chemistry and the speed and efficiency of drug development in general.
OPEN REVIEW, ICLR 2018, 27 March 2018
Exploring deep recurrent models with reinforcement learning for molecule design
Daniel Neil, Marwin Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, Nathan Brown
The essence of molecular design is to effectively fulfill a molecular property profile that is desirable as a drug. In this paper we consider a number of different generative models for the design of new molecular structures the satisfy specific multiple objectives that are desirable for a particular drug discovery project. In addition to the evaluation of multiple generative models, we also presented as part of this work a benchmarking dataset to the community with the aim to provide an objective set to evaluate other new de novo molecular design models appropriately
ChemMedChem, 20 March 2018
Special Issue: Cheminformatics in Drug Discovery
Andreas Bender, Nathan Brown
BenevolentAI guest edited a special issue of ChemMedChem in early 2018 with our Head of Cheminformatics, Nathan Brown, in collaboration with Andreas Bender at the University of Cambridge. The special issue consisted of twenty original research papers from leading names in the field and was introduced with a guest editorial written by Nathan and Andreas, introducing the content. The special issue covered a broad range of topics in Cheminformatics from recent work in Machine Learning in Drug Discovery, to large scale data analyses of protein structures and ligand binding.