Written by : Oswald Yap Tingzhe on Digilah (Student Research)
My Bio
I am a 2nd year student at Nanyang Technological University. The inspiration for this research article is the rapid and advanced development of AI and CRISPR technology in the medical field. AI’s rapid data analysis transforms drug development, while CRISPR precision revolutionizes genetic modification. Together, these two incredible technologies result in groundbreaking developments, shaping the future of healthcare with innovative treatments and personalized medicine.
Read my article below:
Section 1: CRISPR Technology
CRISPR technology is derived from bacterial immune systems to facilitate precise gene modification. It is made up of short repetitive DNA sequences that contain “spacer” sequences, which contain viral genetic information.
By utilizing Cas9 enzyme as molecular scissors, RNA molecules can guide CRISPR to target and edits specific genes precisely. With this capability, scientists can introduce new genes or modify existing sequences accurately.
CRISPR has a profound impact on treating genetic disease, because it can modify faulty genes that are responsible for these hereditary conditions. Therefore, CRISPR can treat diseases that cannot be treated through therapeutic interventions.
This makes CRISPR a revolutionary tool in the pursuit of precise and effective treatments for many genetic diseases. This is due to its precision, versatility, and transformative impact.
A group of scientists from China has demonstrated that CRISPR can eliminate or inactivate carcinogenic viral infection (cancers are caused by gene mutations). They have proven that CRISPR can treat cancer when it is applied to human viruses, such as hepatitis B virus and HPV16.
For example, HPV16 and HPV18 viruses can induce cervical cancers by Papillomavirus E6 and E7 viral proteins. Bacterial CRISPR/Cas RNA guided endonuclease can be reprogrammed to HPV-transformed cells to knockout (delete) E6 and E7 genes.
One of the clinical challenges faced is the off-target effects even though CRISPR can edit and modify gene precisely. Many researchers have reported that the CRISPR-Cas9 technology cause gene modification in other undesired genomic loci. As a result, this will reduce the efficacy of gene modification.
To reduce the off-target effects of CRISPR-Cas9, a scientist from Harvard university has modified the Cas9 protein to enhance the recognition of target DNA. Hence, it can improve the on-target specificity and efficiency of CRISPR-Cas9 technology.
Section 2: AI accelerates drug development
The advanced algorithms of artificial intelligence (AI) can revolutionize the development of drug by analyzing extensive dataset with speed and accuracy.
These algorithms can identify potential drug candidates more efficiently than traditional methods. Due to its ability to identify intricate pattern and relationships within the extensive and diverse data, it can lead to more informed decision-making.
The complex algorithm in AI can also reduce the time and resources required for early-stage research. Hence, this innovative application of AI marks a paradigm shift, creating the hope of streamlining drug discovery to bring novel and effective treatments to patients more swiftly.
AI has emerged as a possible solution to the problems caused by chemical space of atoms in the pharmaceutical industry.
The AI algorithms have been increased in computer-aided drug design (CADD) due to the development of technologies and high-performance computer.
The two most common methods of CADD are structure-based drug design and ligand-based drug design. The structure-based drug design analyzes the three-dimensional of proteins, while the ligand-based CADD uses the information of studied active and inactive molecules.
Machine learning computational algorithms, such as support vector machine (SVM), has ensured to improve the activity of bioactive components.
The combined methods of both deep-learning and machine-learning has increased the ability, strength, and standard of the evolved products.
In the field of orthopedics, the large amount of data with the inclusion of ML has helped orthopedic surgeons in many aspects of the application.
For example, the advances in this field to assess the impact on the musculoskeletal system of human beings. This is done to provide value-based healthcare and serving the patients in a better manner.
Section 3: Synergy of CRISPR and AI
The integration of CRISPR and AI has led to a new era of unprecedented advancements in the development of drug discovery.
As a result, many critical challenges can be addressed. There will be more novel solutions and the pace of scientific breakthroughs will be accelerated.
The synergy of CRISPR and AI potential drug targets to be identified rapidly in the process of drug discovery and the assessment of their therapeutic viability.
It would be impossible for human researchers to decipher the massive genomic information. AI algorithms make it possible due to its ability to analyze vast data sets, identifying patterns and relationships.
Furthermore, AI has an important role in optimizing the process of predicting the outcomes of genetic editing in CRISPR experiments.
This is because the algorithms can anticipate the effects of specific gene edits after studying the previous CRISPR data. In this way, it can learn from past experiments.
The ability of AI to predict results in experiments not only speeds up the experiment, but also reduce risks to increase accuracy. Therefore, the synergy of CRISPR and AI can revolutionize the landscape of biotechnology.
For example, machine-learning (ML) models are trained using existing datasets and can be used to predict the on/off-target effects of the testing datasets (genomic information).
The current ML models are based on regression-based methods, classification-based methods, and ensemble-based methods.
The advanced ML models enable deep-learning (DL) methods to be applied in the CRISPR-Cas9 system. The models in CIRSPR-Cas9 system consists of multiple layers of interconnected compute units.
The algorithm takes the encoded gRNA-DNA sequence in length 23 in the matrix as input. The convolution layer applies various filters of different sizes to the input matrix.
The next layer performs batch normalization to the output of convolution layer to boost learning and prevent over-fitting.
The last layer (pooling layer) further filters the normalized data from the previous layer. The output of this layer is then passed through multiple layers of deep learning neural network.
The last layer of this network passes the result to the stop layer that will predict whether the input is off-target or on-target.
Conclusion
In conclusion, the combination of CRISPR and AI has led to a revolutionary era in biotechnology. The coupling of the precision of CRISPR in genetic modification and AI in drug development has resulted in a groundbreaking development in drug discovery.
This showcases the transformative potential of this dynamic collaboration.
References:
https://sci-hub.se/https://doi.org/10.1093/bfgp/elaa001
https://link.springer.com/article/10.1007/s11030-021-10217-3
https://link.springer.com/article/10.1186/s12967-022-03765-1#Sec14
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