Flame: an open source framework for model development, hosting, and usage in production environments

Manuel Pastor,Ferran Sanz,José Carlos Gómez-Tamayo

doi:10.1186/s13321-021-00509-z

Abstract

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

Highlights

In the last years, biomedical data is becoming widely available, thanks to the creation of repositories like PubChem [1] and ChEMBL [2], databases resulting from public–private partnerships like eTOX [3, 4], as well as data policies like FAIR [5], which facilitate the access of existing data to the scientific community
We introduce Flame, a new modeling framework for facilitating the development, hosting, and use of predictive models in production environments
In Flame, the methods used to build a model and their configurable parameters are defined in a single parameter file, which can be seen as the model blueprint

Summary

Introduction

Pharmaceutical companies can extract these series from their own internal repositories and use them isolated or combined with compounds from external sources. This fact, combined with recent developments in machine learning (ML) and deep learning (DL) methodologies [8] as well as with the implementation of many of these methods in open source libraries [9], create an ideal scenario for the development of predictive models with biomedical application. A few remarkable models developed recently have been listed in Table 1 as examples of applications of this methodology, illustrating their usefulness. The true capability of a model for solving real-world problems critically depends on aspects related to model implementation, as the following

Methods

Results

Discussion

Conclusion