Activity Prediction

1. Activity Prediction In drug discovery, researchers are not only interested in detecting active molecules, but also in determining the biological activity observed in a particular molecule. At the same time, it is useful to identify the most representative subset of molecular descriptors to help determine the attributes required in drug design. Due to the flexibility of the molecular structure, a molecule may adopt a wide range of conformers and identifying the bioactive conformers is very important for understanding the recognition mechanism between small molecules and proteins, which is crucial in drug discovery and development. BOC Sciences is a professional company to provide activity prediction with several methods. Our advanced techniques and experienced scientists enable us to provide high-quality and cost-effective data to our customers. Multiple-instance Learning Multiple-instance learning (MIL) is a machine learning method capable of predicting bioactive conformers by using a variant of supervised learning. In the MIL framework, each instance is recognized as a feature vector, which commonly resides in the high-dimensional feature space. The high dimensionality may offer significant information for learning tasks, but at the same time, it may also include a great deal of irrelevant or redundant features, which might negatively affect learning performance. So, reducing the dimensionality of data is the priority to facilitate the classification task and improve the interpretability of the model. By using MIL, it is possible to predict the contributions of individual conformers for each molecule and identify the putative bioactive conformer. The unique characteristics of MIL make it very useful for the pursuit of biologically significant conformers. In the context of drug activity prediction, the observed biological activity is associated with a single molecule

2. without knowing which conformers are responsible. Furthermore, a molecule is biologically active only if at least one of its conformers is responsible for the observed bioactivity, and the molecule is inactive if none of its conformers is responsible. As shown in Fig. 1., M1, M2, M3 and M4represent the molecules, which are circled by dashed lines. Molecules 2, 3, and 4 have biological activity since they have at least one bioactive conformer, whereas molecule 1 is inactive since none of its conformers is bioactive. Fig. 1. Cartoon representation of the relationship between molecules and conformers. Probabilistic approaches The most appropriate approaches in activity prediction are based on the theory of probability. Generally, virtual screening method is used for selecting hits with single required activity, while the final aim of pharmaceutical R&D is identifying safe and potent lead. To overcome this problem, a method has been developed for prediction of many kinds of biological activity simultaneously based on the structural formula of chemical compound, which is realized in PASS (Prediction of Activity Spectra for Substance). PASS is a computer program that can predict numerous pharmacotherapeutic effects, mechanisms of action, interaction with the metabolic system, and specific toxicity for

3. drug-like molecules with reasonable accuracy on the basis of their structural formulae. If you are looking for a company that offers activity prediction, please contact us without hesitation. BOC Sciences would listen carefully to your needs, and guarantee the most satisfied solutions. Reference 1. Fu, G.; et al. Implementation of multiple-instance learning in drug activity prediction. BMC bioinformatics, 2012, 13(15): S3