AT
Paid
FEATURED
Atomwise (AtomNet®)
Structure-based deep learning platform for drug design.
Uses convolutional neural networks ( $CNN$ s) to predict binding affinity between small molecules and target proteins, allowing for virtual screening of billions of compounds for diseases like cancer and infectious diseases.
PROS
- + Significantly accelerates drug discovery timelines
- + improving hit rates up to 10
- + 000x
- + High accuracy in predicting molecular interactions using deep learning models
- + Capable of screening vast chemical libraries (e.g.
- + over 16 billion compounds)
- + Helps unlock and address historically undruggable protein targets
- + Reduces the high costs associated with traditional experimental trial-and-error methods
CONS
- - High barrier to entry due to custom enterprise/collaboration-based pricing model
- - Requires substantial domain expertise in computational chemistry and biology for effective use
- - Efficacy depends on the quality and availability of the 3D protein structure data
-
Virtual screening of billions of small molecules for hit identification
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Predicting binding affinity between drug candidates and target proteins
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Accelerating preclinical drug development and lead optimization
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Designing novel small molecule treatments for diseases like cancer and Ebola
-
Exploring novel chemical spaces for undruggable protein targets
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