Artificial intelligence-aided screening could boost speed of new drug discovery

Using a natural language-inspired technique, researchers at the University of Central Florida, US, developed an interpretable and generalisable drug target interaction model that achieves 97 percent accuracy in identifying drug candidates for a broad variety of target proteins. Here, Dr Ozlem Ozmen Garibay and Aida Tayebi, who worked on the study, outline their work and how their findings could shape drug discovery.

Drug target interaction (DTI) prediction tasks performed in vitro can be expensive and time consuming. In silico approaches have been used to reduce both cost and time to discover drugs virtually by screening previously known drugs for new treatments and new purposes. This is also known as drug repurposing. Virtual screening reduces the vast molecular interaction landscape to focus the further discovery on potentially promising candidate drugs. Additionally, it can also accelerate the drug discovery process for a new target and disease by repurposing previously known drugs that have already passed clinical trial studies for their effectiveness, safety and side effects and are therefore approved by the US Food and Drug Administration (FDA). Computational screening narrows the list of candidate drugs for further in vitro and in-lab experiments.

A new artificial intelligence (AI)-based DTI model developed by researchers at the University of Central Florida, has sped up the drug screening process against the COVID-19 virus. This research, published in Briefing in Bioinformatics,¹ was conducted through an interdisciplinary collaboration between computer scientists and material scientists. This model, known as AttentionSiteDTI, is inspired by models developed for sentence classification in the field of natural language processing (NLP). It is also the first model that uses the pair of drug and target as a biochemical sentence, with relational meaning between protein pockets and drug molecules which is the key to capture the most valuable contextual semantic or relational information of the sentence. Furthermore, the AttentionSiteDTI model enables an end-to-end graph convolutional neural network model that learns embeddings from the graphs of small molecules and proteins which are not fixed and are sensitive to context similar in NLP.

The researchers outperformed other state‑of‑the‑art studies in predicting the interaction between drug and target and have identified candidates by using deep learning with a self‑attention mechanism to extract the features that rule the most in the complex interaction. They have proved high interpretability through the self-attention mechanism by focusing on the most important parts of the protein interacting with the drug compounds (binding sites); for example, those that contribute the most towards the interaction and high generalisability through the protein input representation that uses protein pockets in the form of graphs.

This is a critical step in the design and development of new drugs to know which biological properties of the compound governs the interaction. According to the study, a benefit of utilising graph convolutional networks is their robustness to different orientations of the three‑dimensional (3D) structures of proteins, however a drawback to this is to find high-quality 3D protein structure.[…]