21. April 2020 18:04
Artificial intelligence has garnered immense applications in various industries including banking, manufacturing, and healthcare. A branch of healthcare that is benefitting from Artificial Intelligence is the ‘Pharmaceutical Industry’. It is met with new challenges in the face of new viruses, mutated antigens, drug-resistant strains, etc. on a daily basis. Additionally, it has become common to see the rendition of once eradicated diseases such as polio. Under these conditions, traditional R&D can be very time consuming and costly.
Traditional drug discovery methods are objective driven and work well for targets whose structure and interactions in the cell are understood. However, most of the cellular transactions have complex pathways.
In order to overcome these challenges, Artificial Intelligence-powered drug discovery offers an effective alternative. Following are the ways in which AI transforms the drug discovery process:
- AI-powered drug discovery follows a data-driven approach that is based on the vast patient datasets. The data is studied and categorized by complex algorithms into understandable information for facilitating drug discovery at a faster pace.
- It applies machine learning to study new incoming data for recognizing new opportunities and information.
- The algorithms search through vast databases of compound structures to identify a compound that can bind to the antigen protein (even if the structure of the target protein has not been yet identified). This saves a lot of time when compared to manual screening of compounds that can act as drug candidates.
How It Works?
- The first step is to take sample from people with and without a disease. Also, samples are taken from people who are at different stages of disease progression.
- The sample data is then extracted into genomics, proteomics, metabolomics and lipidomics for identifying the target.
- AI and Machine Learning software study the information to identify any differences between the disease and non-disease states, proteins, and other features that may impact the disease state.
- The identified proteins and metabolites are considered to be target candidates by the software.
- The candidates are then queried against databases of patents, publications, chemical libraries, clinical trials, and approved drugs. This facilitates a precision-medicine approach by offering a means to triage the patients in an in-silico manner before entering a clinical trial to determine the effectiveness of a potential drug.
Benefits Of AI-Driven Drug Discovery:
- AI does not rely on predetermined targets which rules out the chances of subjective bias.
- AI amalgamates the latest technology in biology and computing to develop algorithms for drug discovery.
- AI offers high predictive power to define meaningful interactions in drug screening. This reduces the chances of pursuing false potential drugs.
- AI moves drug discovery to a virtual lab where screening results can be obtained at a faster pace and efficiency.
For more information on transforming drug discovery through AI, call Centex Technologies at (972) 375 - 9654.
14. November 2018 14:55
Machine Learning (ML) is a technique of artificial intelligence that gives a computer system the ability to learn from data without being specifically programmed. The term machine learning was coined by Arthur Samuel in 1959. It builds algorithms that can learn from data and make data driven predictions on the basis of inputs made in the past. The computer, software as well as the device performs via cognition which works very similar to the human brain.
Here are some most common applications of Machine Learning that we make in everyday life:
- Virtual Personal Assistant: They act as your personal assistants and help you find information as demanded over voice. Machine learning is a vital part of these personal assistants as they collect, define and predict information on the basis of inputs made in the past. They start recognizing your preferences and show results accordingly.
- Traffic Predictions: Our current location is tracked through GPS navigation, which enables the server to predict current traffic. It will do a congestion analysis and let you know the way you should take to avoid being stuck in traffic. Also it estimates the areas which are more prone to traffic congestion on a daily basis.
- Online Transportation Network: Machine learning plays a major role when it comes to online transportation network. For example, it is only though this technique that when you book a cab, the app estimates price of the ride, minimizes the detours, predicts the price when the demand is high etc.
- Video Surveillance: Nowadays, video cameras are designed in a way that makes it possible to detect & prevent any crimes. Machine learning enabled system keeps a track of any unusual activity or behavior & alerts the human attendants if there is something suspicious to help in preventing it.
- Social Media Services: Social media platforms are also using machine learning. It is being used for effectively targeting ads, personalizing the news feed, recognizing the face when you upload your and a friend’s picture, suggesting people you may know on the basis of friends you connect to, profiles you visit, interests you share, etc.
- Online Customer Support: Websites nowadays give people an option to chat with their customer support representative or a chatbot while scrolling through it. The chatbot is able to understand user queries only due to the machine learning algorithms.
- Product Recommendations: Machine learning enables websites & apps to personalize the customer experience and make recommendations as per your taste. It understands the behavior, brand preferences and shows you the results accordingly.
- Online Fraud Detection: It has helped in increasing cyber security by tracking & preventing monetary frauds such as money laundering etc.
For more information about machine learning & its applications, contact Centex Technologies at (972) 375 - 9654.