Revolutionizing AI in the Life Sciences Industry Using Open AI’s GPT-3

Gayathri Nadella 27 Feb, 2023
8 min read

Introduction

Artificial Intelligence has seen enormous advancements in recent years, notably in the life sciences sector. Various fields of the life sciences, like Biotechnology, Pharmaceuticals, and Medical devices, could be transformed by using AI. This article explains how GPT-3 revolutionized AI in the Life Sciences Industry.

GPT-3 by Open AI

Source: NBC News

The recently released Generative Pretrained Transformer 3 (GPT-3), in particular, has the potential to increase the impact of AI in the life sciences significantly.

This article was published as a part of the Data Science Blogathon.

Table of Contents

Current Applications of AI in the Life Sciences

The life sciences industry is already using AI in various ways, in the fields of drug development, diagnostics, and personalized medicine, showing some of the most promising uses.

  • Drug Discovery: To find novel drug targets, forecast drug efficacy and toxicity, and speed up the drug development process, AI is used to analyze large volumes of data. In order to find prospective drug targets and forecast medication efficacy and toxicity, for instance, AI algorithms may analyze enormous volumes of genetic data, which can significantly speed up the drug development process. Diagnostics: AI is used to assist with diagnosing diseases and conditions. For example, AI algorithms can be used to analyze medical images to detect abnormalities like cancerous tumors. AI may also be used to evaluate patient data to forecast the likelihood of specific diseases and ailments, which can aid medical personnel in making better decisions regarding patient treatment.
  • Diagnostics: AI is used to assist with diagnosing diseases and conditions. For example, AI algorithms can be used to analyze medical images to find abnormalities like cancerous tumors. AI can also be used to evaluate patient data to forecast the likelihood of specific diseases and ailments, which can aid medical personnel in making better decisions regarding patient care.
  • Personalized Medicine: AI is used to tailor medical treatment based on a patient’s unique genetic makeup, lifestyle, and environmental factors. AI algorithms, for instance, can be used to evaluate patient data to forecast the effectiveness and adverse effects of particular medications, which can aid medical personnel in selecting the best course of treatment.
Current Applications of AI in the Life Sciences

Source: Scientific American

Current Applications of AI in the Life Sciences with GPT-3

Source: Omnia Health

The Potential of GPT-3 in the Life Sciences

  • The most recent version of OpenAI’s generative language model, GPT-3, has the power to fundamentally alter the way AI is used in the life sciences. The deep learning algorithms used by GPT-3 to produce human-like text have been trained on enormous amounts of data, enabling it to produce excellent text on a wide range of subjects. The processing and comprehension of data in the life sciences might be fundamentally changed by GPT-3. GPT-3 has the potential to completely alter how data is processed and understood in the life sciences.
  • In the life sciences, GPT-3 has the potential to revolutionize how data is analyzed and interpreted. For example, GPT-3 can be used to analyze large amounts of genomic data to identify potential drug targets and predict the efficacy and toxicity of drugs. This can greatly speed up the drug development process and make it more efficient. Additionally, GPT-3 can be used to analyze medical images to detect abnormalities like cancerous tumors, which can assist with early detection in patients and improve their chances of early recovery.
  • In particular, GPT-3 finds many use cases in this field, and with time we will see more and more applications of GPT-3 in one of the areas where AI and the life sciences intersect in the future in the field of precision medicine. Precision medicine seeks to tailor medical treatment based on patient’s unique genetic and molecular profiles. AI, and specifically GPT-3, can play a key role in assisting this field by analyzing patient data to predict the efficacy and side effects of specific drugs and generate personalized treatment plans.
  • Another example of future interaction between AI and the biological sciences is the development of new medical devices and technologies. AI may be used to evaluate data from medical devices to find patterns and trends that might help create new and better medical devices. In order to build new wearable devices that can detect and monitor certain disorders, AI may be used, for instance, to analyze data from wearable fitness trackers to find patterns in physical activity and sleep patterns that are related to particular health issues.
  • Finally, the field of public health is where AI and the life sciences converge. Artificial intelligence (AI) may be used to evaluate data from many sources, such as social media, environmental data, and virtual medical histories, to find patterns and trends connected to certain health issues. The development of public health initiatives and programs to enhance the health and well-being of populations can then be done using this information.

Open AI’s API

Open AI’s API can be used to understand or generate natural language or code.

Invoking GPT-3 model from Open AI’s API using Python

GPT-3 can be invoked using the OpenAI python library.

After creating an account on OpenAI’s website, you can find or create an API key under API keys in order to access the API.

Here is an illustration of how to use Python and the GPT-3 API to generate text.

import openai
# Set up API key
openai.api_key = "YOUR_API_KEY"

# Generate text
model_engine = "text-davinci-002"
prompt = "Write a short story about a cat."

completions = openai.Completion.create(
    engine=model_engine,
    prompt=prompt,
    max_tokens=1024,
    n=1,
    stop=None,
    temperature=0.5,
)

message = completions.choices[0].text
print(message)

The OpenAI library is imported, and the API key is set in this example. The model engine variable is set to the name of the GPT-3 model for use, for example – “text-davinci-002”. The user can enter a text input which can be used to generate the text; for this example, we hardcode the text “Write a short story about a cat.” The method to create from openai. Completion is then used to generate the text, and the parameters are set, like max number of tokens to generate and the temperature for the generation. The generated text is stored in the message variable and is then printed to the console.

It is important to note that the GPT-3 API is very powerful and expensive. Using the API wisely and only when necessary is recommended, as the costs can quickly increase. Additionally, It is important to understand the API’s limitations and biases and use it ethically and responsibly.

Examples of GPT-3 code in Python for the Life sciences Industry

In the life sciences, GPT-3 can be used to automate tasks that are time-consuming and repetitive for humans, such as data analysis and summarization.

Examples of GPT-3 code in Python for the Life sciences Industry

Source: Pharmaceutical Online

Example 1:Consider that you have a large genetic sequences dataset and want to summarize the data meaningfully.

This task can be automated using the following code with the help of GPT-3:

import openai
# Set up API key
openai.api_key = "YOUR_API_KEY"

# Define the prompt
prompt = "Please summarize the data in the following table:\n"
prompt += "Gene Sequence\tExpression Level\n"
prompt += "ABC123\t\t\t\t\t\tHigh\n"
prompt += "DEF456\t\t\t\t\t\tLow\n"
prompt += "GHI789\t\t\t\t\t\tModerate\n"

# Generate summary text
model_engine = "text-davinci-002"
completions = openai.Completion.create(
    engine=model_engine,
    prompt=prompt,
    max_tokens=1024,
    n=1,
    stop=None,
    temperature=0.5,
)

summary = completions.choices[0].text
print(summary)

The openai library is imported, and the API key is set in this example. The prompt variable contains a table of gene sequence data, and the model_engine variable is set to the name of the GPT-3 model to be used. The openai.The completion. create method is then used to generate the summary of the data, with various parameters such as the maximum number of tokens to generate and the temperature setting for the generation. The generated summary is stored
in the summary variable and is then printed to the console.

GPT-3 can also be used to generate reports, presentations,
and other forms of written communication in the life sciences.

Example 2: Consider a scenario where you need to prepare a report on the latest advances in gene editing. With the help of GPT-3, it is automated using the following code:

import openai

# Set up API key
openai.api_key = "YOUR_API_KEY"

# Define the prompt
prompt = "Please write a report on the latest advances in gene editing.\n"

# Generate report text
model_engine = "text-davinci-002"
completions = openai.Completion.create(
    engine=model_engine,
    prompt=prompt,
    max_tokens=1024,
    n=1,
    stop=None,
    temperature=0.5,
)

report = completions.choices[0].text
print(report)

In this example, the process is similar to the previous one. The OpenAI library is imported, and the API key is set. The prompt contains the text for the report, and the model engine variable is set to the name of the GPT-3 model to be used. The openai.Completion. create method is then used to generate the report with similar parameters. The generated report is stored in the report variable and is then printed to the console.

"

Source: Bio Spectrum Asia

Example 3: Another potential application of GPT-3 in the life sciences is a drug discovery and development. For example, GPT-3 can be used to predict the efficacy of a new drug candidate based on its molecular structure. The following code demonstrates how this can be done using the RDKit library and the openai library:

import openai
import rdkit
from rdkit import Chem
from rdkit.Chem import Draw

openai.api_key = "YOUR_API_KEY"

# SMILES representation of the molecule
smiles = "CCCCCCCCCCCCCCCCCCCCCCCCCCCC(=O)OC(C)(C)C"

# Convert SMILES to a molecule object
mol = Chem.MolFromSmiles(smiles)

# Visualize the molecule
img = Draw.MolToImage(mol)
img.show()

# Use OpenAI's GPT-3 to predict the efficacy of the drug based on its molecular structure
model_engine = "text-davinci-002"
prompt = f"Predict the efficacy of the drug with molecular structure represented 
by the SMILES string '{smiles}'"

completions = openai.Completion.create(
    engine=model_engine,
    prompt=prompt,
    max_tokens=1024,
    n=1,
    stop=None,
    temperature=0.5,
)

message = completions.choices[0].text
print(message) 

This is an example of how you could use RDKit and OpenAI GPT-3 model (text-davinci-002) to predict the efficacy of a drug based on its molecular structure:

In this example, we first use the rdkit library to convert the SMILES string representation of the molecule into a rdkit.Chem. rdchem.Mol object. Then, we visualize the molecule using the rdkit.Chem.Draw module. Finally, we use OpenAI’s GPT-3 model to predict the efficacy of the drug based on the structure at a molecular level by sending text data to the openai.Completion. create method. The resulting text generated by the model is stored in the message variable and can be displayed using the print function.

Conclusion

By automating time-consuming and repetitive processes for people, GPT-3 has the potential to revolutionize the life sciences sector by freeing up critical time and resources to concentrate on more significant elements of research. To utilize AI to its full potential in the life sciences, ethical constraints must be addressed through dialogue between industry experts, academic researchers, and policymakers to ensure AI is used responsibly and equitably. In conclusion, the applications of GPT-3 in the life sciences industry are vast and varied. From data analysis and summarization to report writing and drug discovery, GPT-3 has the potential to revolutionize the way we work in the field of life sciences. By leveraging its language generation capabilities, we can automate time-consuming and repetitive tasks for humans, freeing up valuable time and resources to focus on more important aspects of our research. We may expect to see even more cutting-edge AI applications in the life sciences in the future as the technology develops.

The Key Takeaways of this article are as follows:

  1. AI is used in several life sciences areas, including drug discovery, diagnostics, and personalized medicine.
  2. GPT-3 has the potential to enhance the impact of AI in the life sciences by analyzing large amounts of genomic data, medical images, and patient data to generate high-quality personalized treatment plans.
  3. OpenAI’s API can be used in life sciences to enhance the capabilities of GPT-3 by generating personalized treatment plans and analyzing large datasets.
  4. The future intersection of AI and life sciences could include developing new medical devices, technologies, and public health interventions.

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Gayathri Nadella 27 Feb, 2023

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