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Drug Discovery Through Artificial Intelligence

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.

AI & Customer Service: The Future

Customer service is an important aspect of any business organization. Businesses keep looking for new ideas to improve their customer service and offer better customer experience, which has led to the advancements in AI based customer service solutions. Although, businesses have been using AI in customer support for a while; the collaboration still holds many facets that are yet to be unfolded in the future.

Following are some of the applications of AI that can be explored for enhancing customer service by businesses:

  • Brand Messenger: In recent years, there has been an increased user inclination towards messaging apps. The use of messaging has extended from personal communications to user engagement with brands. This has laid out a path for businesses to incorporate chatbots to interact with new and existing customers. As some major industries (like fashion, tourism, food chains, airline, e-commerce and hotels) have adopted this feature to increase user engagement; it would be exciting to know which industries will follow the suit in future.
  • Quick Resolution: Wait time for resolving simple queries is an important determinant in customer satisfaction. Customers seek quick answers to general queries and tend to trust a brand that offers faster answers and streamlined action plans for their queries. Thus, businesses can exploit the capacity of AI to multi-task and handle multiple automated queries. This will help in limiting the response time and generating accurate resolutions.
  • Customized User Experience: In addition to making self-service user interfaces more intuitive, AI can help in anticipating customer needs based on previous chat history, contexts and user preferences. AI integrated systems can capture a large amount of data for identifying customer issues, defining customer behavior, determining frequent decisions, prompting with proactive alert messages, suggesting personalized offers and discounts, etc. Such intelligent assistance and pre-emptive recommendations will help companies in offering a quality rich customer service.
  • AI Controlled Multiple Support Channels: In addition to providing direct assistance to the customers, AI can be used to control multiple channels of customer support. For example, in case a telecommunications agent is unable to answer a query, AI can determine the issue and direct the customer towards dedicated support channel.

Undoubtedly, these applications support the strengthening of collaboration between AI & Customer Support. However, as the AI systems rely on collecting extensive user data for working efficiently, this gives rise to privacy concerns. The data collecting system can be compromised resulting in a data breach. Thus, business organizations need to pay due attention to data security policies before implementing AI supported customer service systems.

For more information about use of AI in customer service, call Centex Technologies at (972) 375 - 9654.

Understanding The Difference Between AI, Machine Learning & Deep Learning

Artificial Intelligence (AI), Machine Learning and Deep Learning are commonly used interchangeably. However, in technological context; Machine Learning and Deep Learning are subsets of AI. In order to understand the difference between these terms, it is important to know the actual meaning of individual term.

Artificial Intelligence (AI): Artificial Intelligence is a term that defines the simulation of human intelligence processes by computer systems. The processes include learning, reasoning and self-correction. AI is broadly classified as weak (narrow) AI and strong AI. Weak AI systems are designed to do a particular task. The most common example of weak AI is the virtual personal assistants. On the contrary, strong AI systems are equipped with generalized human cognitive abilities. These systems are able to find a solution to any problem independent of human intervention.

Machine Learning (ML): ML is an application or subset of Artificial Intelligence. Under this application of AI, a machine is programmed to access and manipulate data. The machine can analyze the data to identify patterns and learn from these patterns. This allows the machine or computer system to modify decisions as per any change in data without explicit programming. Machine Learning is driven by algorithms and stat models. The common usage of Machine Learning can be found in apps such as email filtering, optimization, internet fraud detection, etc. Machine Learning methods are widely grouped as supervised and unsupervised ML.

  • Unsupervised ML: These methods group interpret data based only on input data. Clustering methods are an example of unsupervised ML.
  • Supervised ML: Supervised ML methods use both input and output data to develop a predictive model. Classification and Regression methods are listed as supervised ML.

Deep Learning (DL): It is a broader subset of AI. Deep Learning involves collection of large unstructured data and combing through it to generate classified structured information. The basic difference between Machine Learning and Deep Learning is that ML is task oriented learning, whereas DL is more general. It is used to derive meaning or identify patterns in unstructured data. This, in turn, helps in spotting large scale trends or irregularities. Some common applications of Deep Learning include self-driving cars, fraud news detection, natural language processing, visual recognition, etc.

For more information about Artificial Intelligence, Machine Learning and Deep Learning, call Centex Technologies at (972) 375-9654.