The next step in deep learning-guided clinical trials

Krittanawong, Chayakrit

doi:10.1038/s44161-022-00044-6

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…A DL solution aiming at outputting marketable devices should also account for non-biological variables, such as the ones influencing production costs or clinical response. Data necessary to inform such models is however lacking, although improvements are underway, such as for clinical trial design [127].…”

Section: Discussionmentioning

confidence: 99%

From sequence to function through structure: deep learning for protein design

Ferruz

Heinzinger

Akdel³

et al. 2022

Preprint

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The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be leveraged to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.

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Section: Discussionmentioning

confidence: 99%