BenchSci vs. ACD/Labs Percepta Platform

Optimize the entire selection process for reagents and model systems to eliminate inefficiencies and reduce errors that can result in costly experimental setbacks. By condensing the decision-making timeframe from the usual 12 weeks to a mere 30 seconds, project timelines can be significantly shortened. This strategy not only minimizes direct expenses associated with consumables but also has the capacity to generate substantial annual savings. In addition, it supports the organizational mission by allowing scientists to reclaim precious research time. Experience concrete business benefits from AI through a solution that is both proven and ready for implementation. Over 41,200 scientists, representing 15 of the top 20 pharmaceutical companies and more than 4,450 academic institutions, rely on BenchSci’s AI-Assisted Antibody Selection to design more effective experiments while achieving significant reductions in hard costs. It’s crucial to recognize that antibodies account for only 40-50% of the reagent-related failures. Gain access to a wealth of experimental evidence, an extensive catalog of reagents and model systems, and independent validations, all within a single intuitive interface. Leverage real-world experimental data sourced from 11.2 million scientific publications, including those often not publicly available, for a comprehensive approach to research. This cutting-edge methodology equips researchers with the tools necessary to make swift and informed decisions, ultimately transforming the landscape of scientific experimentation. By streamlining processes and enhancing data accessibility, the opportunity for breakthroughs in research becomes significantly more attainable.

Percepta provides a comprehensive platform for calculating a range of physicochemical properties, as well as ADME characteristics and toxicity endpoints. Users can seamlessly analyze outcomes from its various calculators, with each module offering targeted information, features like structure highlighting, and specific calculation methods. Additionally, the platform includes robust filtering, sorting, and graphing functionalities, which facilitate the examination of predicted outcomes. To gauge the reliability of these predictions and their applicability to your current endeavors, you can utilize metrics such as reliability index, probability assessments, and comparisons with similar structures from established training sets. The data on calculated molecular properties can also be leveraged for studying structural modifications and optimizing leads to align with the desired product profile concerning absorption, distribution, metabolism, and excretion. Furthermore, incorporating experimental data can enhance your comprehension of the unique chemical landscape while simultaneously boosting the accuracy of predictions through integrated machine learning features. This multifaceted approach not only streamlines research but also fosters innovation in chemical property analysis.