04 Feb Cresset launches Forge V10.6 for molecule design
Cresset is delighted to announce the availability of Forge™ V10.6, their powerful computational chemistry suite for understanding structure-activity relationship (SAR) and new molecule design. The focus of this release is on new and improved methods to generate robust Quantitative Structure-Activity Relationship (QSAR) models with strong predictive ability.
Choose the molecules to make next
Project chemists generally know which molecules they can make with a reasonably good chance of them being active. They often have too many clever ideas and are looking for ways of filtering and prioritizing lists of tangible compounds, arrays and small libraries.
Having a predictive QSAR model is a terrific way of doing this – you send your molecules into the model and get immediate feedback on whether making a compound is a good or bad idea.
However, getting a robust, predictive QSAR model is not always straightforward, and this is still a pain point for many of our users. You need a training data set of reasonable size, good activity data (e.g., pKi, pIC50) spanning a sufficiently large range, good descriptors and good modeling algorithms.
While we can’t help with the need of having a training data set of reasonable size and spread of activity, we can help with the rest.
The new Machine Learning (ML) methods in Forge, namely Support Vector Machines (SVM), Relevance Vector Machines (RVM) and Random Forests (RF) significantly expand the range of available QSAR model building options beyond the previous Field QSAR and k-Nearest Neighbors (kNN) regression options (Figure 1). Having access to a panel of well known, robust statistical tools gives you more opportunities to build a predictive model useful in project work.
Read the full release announcement here.