Needle in a haystack: Harnessing AI in drug patent searches and prediction

The classification codes granted by patent offices are useful instruments for simplifying the bewildering variety of patents in existence. They are singularly unhelpful, however, in locating a specific subgroup of patents such as that of drug-related pharmaceutical patents for which no classification codes exist. Taking advantage of advances in artificial intelligence and in natural language processing in particular, we offer a new method of identifying chemical drug-related patents in this article. The aim is primarily that of demonstrating how the proverbial needle in a haystack was identified, namely through leveraging the superb pattern-recognition abilities of the BERT (Bidirectional Encoder Representations from Transformers) algorithm. We build three different databases to train our algorithm and fine-tune its abilities to identify the patent group in question by exposing it to additional texts containing structures that are much more likely to be present in them, until we obtain the highest possible F1-score, combined with an accuracy of 94.40%. We also demonstrate some possible uses of the algorithm. Its application to the US patent office database enables the identification of potential chemical drug patents up to ten years before drug approval, whereas its application to the German patent office reveals the regional nature of drug R&D and patenting strategies. The hope is that both the method proposed and its applications will be further refined and expanded forthwith.