Artificial intelligence in drug development: present status and future prospects

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

“…In the last years, novel ligandbased methods, including machine learning (ML) models, have been used for F2L campaigns. ML models are statistical methods that present the capacity to learn from data without the explicit programming for this task, and then, make a prediction for new compounds (Mak and Pichika, 2019). The increase of storage capacity and the size of the datasets available, coupled to advances in computer hardware such as graphical processing units (GPUs) (Gawehn et al, 2018), provided means to move theoretical studies in ML to practical applications in drug discovery (Vamathevan et al, 2019).…”

“…More recently, a subfield of ML called deep learning (DL) which utilizes artificial neural networks to learn from a large amount of data have been used to resolve complex problems (Mak and Pichika, 2019). DL models are not only able to learn from a dataset and to make predictions for new data but are also able to generate new data instances through a constructive process (Schneider, 2018).…”

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

“…The international effort to discover or re-purpose drug treatments and vaccines can also benefit from extensive data science work predating COVID-19 [30]. For example, computational methods can reduce the time spent on examining data, predicting protein structures and genomes [31], [32]. It can also assist in identifying eligible patients for clinical trials [33], an often costly and time consuming part of drug development.…”

AI Case Studies: Potential for Human Health, Space Exploration and Colonisation and a Proposed Superimposition of the Kubler-Ross Change Curve on the Hype Cycle

“…Given the driver to improve overall costs associated with research and development for new medicines and the potential of AI, there are very high expectations on the technology to shorten discovery cycle times and to align new drug design with digital data that is, or will be collected on individuals to ensure the 'right drug to the right patient taken in the right way' [45]. A rapid increase in the number of collaborations of AI technology companies with key players in the biopharmaceutical industry has occurred particularly over the last 2-3 years [46]. In addition to drug hunting and alignment to patient needs, AI also has a potential role in maximising the value of established drugs or discontinued drugs to be repurposed for the treatment of human disease for which they were not originally intended.…”