Publications
Entering the Era of Computationally Driven Drug Development
Increasing quality and standardization of experimental methods in preclinical testing have created valuable data sets that improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of quantitative structure–activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integrating machine-learning approaches and new data sets stands to make a fundamental impact on the speed and accuracy of predictions from R&D to approval.
Entering the Era of Computationally Driven Drug Development
Increasing quality and standardization of experimental methods in preclinical testing have created valuable data sets that improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of quantitative structure–activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integrating machine-learning approaches and new data sets stands to make a fundamental impact on the speed and accuracy of predictions from R&D to approval.
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Introducing AtlasGEN™ Novel Drug Designer
AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-lygand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
