AI could threaten pharmaceutical patents

Heuer, Lutz

doi:10.1038/d41586-018-05555-6

Cited by 4 publications

(4 citation statements)

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“…Further relevant questions relate to the question whether an in silico prediction or de novo design can be classed as ap atentable innovation. [84,85] While human expertise may play adeclining role in selecting the meaningful ones from among ap ool of computer-generated molecular structures,t he knowledge and skills of chemists remain essential for putting the majority of the computer-generated designs into practice and perspective-at least for the time being.…”

Section: Are We Nearly There Yet?mentioning

confidence: 99%

Automated De Novo Drug Design: Are We Nearly There Yet?

2019

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Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.

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Section: Are We Nearly There Yet?mentioning

confidence: 99%

Automated De Novo Drug Design: Are We Nearly There Yet?

2019

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“…Considering the AI a collaborative partner rather than a competitor might be advisable 162,163 . This view would also positively contribute to the current debate on the patentability of AIgenerated drugs 164,165 .…”

Section: Challenge 5: Research Culture and The Appropriate Mind-setmentioning

confidence: 84%

Rethinking drug design in the artificial intelligence era

Schneider

Walters

Plowright

et al. 2019

Nat Rev Drug Discov

539

349

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Perspective │2 Artificial intelligence (AI) tools are increasingly being applied in drug discovery. Whilst some protagonists point to vast opportunities potentially offered by such tools, others remain skeptical, waiting for a clear impact to be shown in drug discovery projects. The truth is probably somewhere between these extremes, but it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' for small-molecule drug discovery with AI and approaches to address them.

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“…The fact that some of the bond-constrained routes proposed by Chematica actually mirror patent-circumventing syntheses that were carried out experimentally (e.g., Figure 5G versus Pan et al 30 and Figure 5I versus Liu et al 34 ) or syntheses in which entire structural motifs were kept intact (Figure 7A versus Coldham and Leonori 42 and Figure 7C versus Clemo and Ramage 43 ) implies that our approach emulates the ways in which human experts think about such problems. In a broader context, we believe that the methods we outlined not only will be useful in research practice but also will have a significant impact on how the intellectual property related to synthetic routes is protected or challenged-the former by harnessing the computer's power to enumerate and preemptively patent very large numbers of viable synthetic routes and the latter by navigating around patented methodologies (especially those leaving ''an analytical fingerprint'' in the target; for interesting discussion of this and related patent-law topics, see Pohl, 44 Heuer, 45 and Lowe 46 ). Of course, when applied in industrial settings, the catalogs of the starting materials should be updated to reflect large-scale pricing model and suppliers of a specific company; in practice, such a replacement is a trivial substitution of one text file supporting the program's search algorithms and chemical knowledge base.…”

Section: Discussionmentioning

confidence: 99%