The NEQUIMED/IQSC/USP implements an early vocation initiated by one of its members (A neural network analysis for antileishmaniasis compounds). A new strategy that can help in the process of discovering new drug candidates: Machine Learning in systems that mimic the human brain and allow access to huge amounts of data.
It is an innovative computational strategy that quickly and efficiently “teaches” computers to find new, successful bioactive chemicals in a way that supersedes current computer-based methods.
Machine learning encompasses many types of algorithms-including decision trees, nearest neighbors (but mostly see k-medoids), and neural networks that “learn” from training datasets and then make predictions of the real world using test and validation collections. Deep learning is a sophisticated type of multi-layered neural network that optimizes responses. These layers are composed of nodes (which mimic the neurons of the human brain) and are fired in the presence of stimuli.
Along with neural networks, NEQUIMED/IQSC/USP also employs two other techniques of machine learning, namely random forests (RF) and support vector machine (SVM).
Taken together, the high level of abstraction of the data has allowed to identify new inhibitors of cysteine proteases of interest for different disease models that use these proteins enzymes as markers.
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