Generative AI applications and available open source AI tools in health and life sciences. | By NEERAJ SHRIVASTAVA | July 2023

Generation AI Often refers to a type of artificial intelligence that can autonomously create new content and data by imitating or learning from existing data.

Generative models are trained on large datasets and learn to understand patterns, structure and relationships in the data. Once these models are trained, they can produce new samples or outputs that closely resemble, but are not exact copies of, the training data.

Generative AI works by using neural networks to identify patterns in large data sets. A neural network is a type of machine learning algorithm inspired by the human brain. They consist of many interconnected nodes, each representing a different aspect of the data.

When a neural network is trained on a dataset, it learns how to identify relationships between features and use this knowledge to generate new data that resembles the training data.

These are the areas of health and life sciences where generative AI will have a major impact

• drug discovery and design.
• Protein folding and structure prediction.
• Genomics personalized medicine.
• medical imaging.
• Disease modeling and prediction

Generative models help identify novel drug candidates by generating new molecular structures that exhibit desirable properties. AI models such as generative adversarial networks (GANs) and variational autoencoders (VAEs) can learn from vast databases of known molecular structures to generate new molecules that are potentially effective drugs. By predicting properties such as bioactivity, toxicity, and solubility, generative AI can greatly speed up the drug discovery process, saving time and resources.

Open source tools:-

1. RDKit (https://www.rdkit.org/): RDKit is open-source cheminformatics software that provides a collection of tools for manipulating chemical structures, fingerprinting, and molecular modeling. It contains machine learning algorithms and can be used for tasks such as virtual screening, molecular property prediction.
2. deep chem (https://deepchem.io/): DeepChem is an open-source library that aims to make it easy to apply deep learning techniques to drug discovery and molecular modeling problems. It provides tools for manipulating molecular data, building and training deep learning models, and evaluating their performance.
3. Chemical Development Kit (CDK) (https://cdk.github.io/): The Chemistry Development Kit (CDK) is a free, open-source Java library for cheminformatics. It can be used for various tasks in drug discovery, including structure-based drug discovery, virtual screening, and molecular property prediction.

Understanding protein structure is crucial for studying its function and interactions. Generative AI models, such as DeepMind’s AlphaFold, can predict protein structures with high accuracy by learning from existing protein sequences and their known 3D structures. These predictions can guide the development of new drugs, therapeutics and biotechnology applications.

1. alpha fold : (GIHUB Link ) falls into the category of protein folding and structure prediction in the fields of healthcare, life sciences and drug discovery. Specifically, it is an advanced deep learning model developed by DeepMind that predicts protein structure based on amino acid sequence. By accurately predicting protein structures, AlphaFold helps researchers gain a deeper understanding of protein function and interactions. This is essential for the development of new drugs, therapeutics and biotechnology applications. Due to its high accuracy and efficiency in predicting protein structure, AlphaFold has revolutionized the field of structural biology and has become an invaluable tool for researchers and scientists.
2. Rosetta (https://www.rosettacommons.org/): Rosetta is an open-source software suite for predicting and designing protein structures, protein-protein interactions, and protein-small molecule interactions. We use a combination of search algorithms and energy-based scoring functions to generate and evaluate protein models. 3. I-TASSER (https://zhanglab.ccmb.med.umich.edu/I-TASSER/): I-TASSER is a hierarchical approach to predict protein structure and function. Combine template-based modeling with ab initio folding simulations to predict protein structures. This software is not fully open source, but is freely available for academic purposes.
3. Itasser (https://zhanglab.ccmb.med.umich.edu/I-TASSER/): I-TASSER is a hierarchical approach for predicting protein structure and function. Combine template-based modeling with ab initio folding simulations to predict protein structures. Although not fully open source, this software is freely available for academic purposes.

Generative AI helps identify patterns and relationships in patient data such as genetic information, medical history, and biomarkers to create personalized treatment plans. By analyzing this data, AI models can predict patient response to specific treatments, optimize drug dosages, and identify potential side effects, leading to more effective and safer care. be connected.

1. bioconductor (https://www.bioconductor.org/): Bioconductor is an open source project that provides tools for high-throughput genomic data analysis and understanding. We offer a wide range of packages and resources for working with genomic data that can be used to develop personalized medicine approaches by analyzing gene expression, DNA methylation, and other omics data.
2. Genome Analysis Toolkit (GATK)( https:/gatk.broadinstitute.org/): GATK is an open-source software package for analyzing high-throughput sequencing data, such as whole-genome or whole-exome sequencing. It provides tools for mutation discovery and genotyping that can be used to identify genetic variants associated with disease and treatment response, an important aspect of personalized medicine.
3. Plink (https://www.cog-genomics.org/plink/): PLINK is an open-source genome-wide association analysis toolset. It can be used for tasks such as genotype imputation, association testing, and population structure analysis, helping researchers understand the genetic basis of disease and develop individualized therapeutic strategies.
4. Scikit Conflict Rell (https://scikit-allel.readthedocs.io/): Scikit-allel is an open-source Python library for investigating and analyzing large-scale genomic variation data. It provides tools for manipulating the Genetic 4 data format, performing population genetics analysis, and building machine learning models for tasks such as genotype imputation and phenotypic prediction.
5. hail (https://hail.is/): Hail is an open-source framework for scalable genomic data analysis. It provides a high-level interface for working with genomic data, enabling researchers to more efficiently perform tasks such as quality control, imputation, and genome-wide association studies (GWAS).

Generative AI models can enhance medical imaging techniques by generating high-quality, realistic images from limited or noisy input data. For example, AI algorithms can increase image resolution, fill in missing data, or reconstruct 3D images from 2D slices. These enhancements enable more accurate diagnosis and better treatment planning.

1. NiftyNet (https://www.niftynet.io/): NiftyNet is an open-source convolutional neural network (CNN) platform for medical image analysis and image-guided therapy. It provides tools for training and deploying deep learning models for tasks such as image segmentation, registration, and reconstruction.
2. MONAI (https://monai.io/): MONAI is an open-source PyTorch-based framework specifically designed for healthcare imaging. It provides a collection of tools and methods for building and training deep learning models for tasks such as image segmentation, classification, and registration.

Generative models can be used to simulate disease progression and predict its outcome based on patient data. By learning patterns and relationships between various factors, these models can estimate disease risk, predict disease trajectories, and identify potential preventative measures.

1. epi model (https://www.epimodel.org/): EpiModel is an R package for mathematical modeling of infectious diseases. It allows users to simulate and analyze the spread of disease across populations using different types of models, including deterministic, probabilistic, and agent-based models.
2. GLEAMviz (http://www.gleamviz.org/): GLEAMviz is an open source tool for fashion modeling and visualization. Use the Global Epidemic and Mobility Model (GLEAM) to simulate the spread of disease around the world by incorporating real-world data on population, human movement, and disease transmission.
3. PyMC3 (https://docs.pymc.io/): PyMC3 is a Python library for Bayesian modeling and probabilistic machine learning. It allows users to define custom probabilistic models and perform Bayesian inference for use in disease modeling and prediction. PyMC3 can also be used to generate synthetic data by sampling from learned probability distributions.