Artificial Intelligence (AI) has occupied many fields and most probably there are yet more to come. In every area concerning Big Data, it will prove itself more effective than human deduction. However, the process of decision making by AI is not entirely clear. The absence of the human factor makes machines actually a lot more useful due to their precision and speed of task completion. Its sole emphasis on perception, reasoning, and action overtakes the human possibilities. AI’s core abilities include language understanding, learning, and adaptive systems, problem-solving, visual perception, modeling. Modeling of the natural biological systems and other AI’s abilities enable the enhancement of biotechnology in the fields such as precision medicine or brain-machine interface.
ML’s Ground-Breaking Effect On Drug Discovery And Biotech’s Development
Machine learning (ML) models, a ground-breaking part of AI are predicted to be revolutionary in drug and biology discovery and development. AI contributes to the improvement of drug R&D to a large extent. Learning algorithms on bioactive data supply promising analysis to form statistically-based research hypotheses.
Small molecule effectors are the target to be identified by future molecular medicine and regarded to be crucial in drug discovery. Furthermore, pharmacology networks for phenotypic screening hit either of synthetic or natural origin is the base for drug development. The enormous amount of chemical and biological data associated with the previously mentioned poses an impossible task to be conducted with traditional methods by researchers.
Computer’s Algorithms Competitive Advantage
Human analysis can not be compared with the speed of computer algorithms, which are able to easily identify underlying patterns hardly accessible for the researchers’ capabilities. Specially designed deep-learning algorithms are able to predict pathways of molecules, create chemical entities, which deeply analyze the drug’s impact.
SPiDER software uses a neural network-inspired algorithm that can deduce the relationship between the drug and the target by discovering the similarity of the descriptor to reference ligands in the same neuron without the consideration of the target identity of those reference ligands to validate the studies. Due to the use of synthetically motivated fragments of Archazolid A as bioactivity blueprints, thanks to the reference structures of the software a target inference could have been drawn.
Limitations Of SPiDER
As every technique has its limitations, it is not possible to be applied in every domain. The discussed method can not be used regarding proteins, because the method is based on a similar ligands principle. In a similar manner, as DNA/RNA binders are protein modulators, their target identification is hard to access. Taking into account the possibilities of learning algorithms, even though results may be negative but conclusive, it rises prospects for further development of machine intelligence.
Precision Medicine Approach
Another model revolutionizing the field of healthcare is the precision medicine approach. Contrary to the one-size-fits-all approach, precision medicine is more individual-oriented, taking into account each person’s genes, environment, lifestyle for more accurate treatment. It is still developing its potential by implementing AI to biotechnology in medicine.
The complex patient’s data including lifestyle information, medical history, laboratory and imaging tests, diagnoses, prescribed medications, performed surgical procedures thanks to AI can form a clearer picture and lead to quick and precise treatment, thus, referred to as precision medicine. AI’s modeling ability of complicated biological interactions can contribute to a deeper understanding of biological mechanisms, which then leads to earlier disease detection by optimizing the decision process.
AI And ML Algorithms Crucial In Cancer Rare And Research
Machine learning algorithms using Big Data can be a milestone in cancer care and research worldwide by developing international cancer networks. Access to more data and AI’s contribution is presumed to help to „identify beneficial therapies for rare and highly aggressive cancers, observe different therapeutic outcomes by different parameters, analyze associations of cancer with other disease-specific attributes, discover new cancer etiologies, incorporate pertinent patient and cancer characteristics into clinic-based uses, conduct economic broad-based cancer trials, uncover genomic and molecular events sensitive to existing or new treatments and analyze and develop new treatment pathways”.
Artificial Neural Network And Bayesian Network
An artificial neural network is a tool for analyzing breast cancer through mammographic and demographic data and for analyzing lung cancer through clinical and gene expression data. Other machine learning techniques like Bayesian network, SVMs, graph-based semi-supervised learning (SSL), and decision tree contribute to the prediction of progression of different types of cancer by analyzing biotechnological data. Applying those algorithms thanks to their unique structure, which mirrors the molecule’s interactions, helps to examine the data thoroughly, which results in an accurate conclusion about cancer susceptibility, recurrence, and survival predictions.
Artificial Intelligence And Nanotechnology Upgrading The Brain-Machine Interface
The connection of Artificial Intelligence and nanotechnology is a huge step in brain research and development with the result of creating nanoengineered brain-machine interfaces (BMI). It will enable the restoration of neurological function by invasive technologies, like neural prosthesis and non-invasive technologies, like EEG.
Primary Goal Of Brain-Machine Interface
Brain-machine or brain-machine interface are technologies with the role of communication with the brain, spinal cord, and neural sensory retina. The aim may vary on the technology, however, the primary goal is to restore neural function being the result of trauma or disease. Former can be achieved by recording and interpreting neural signals to follow an intended neural command on an external device.
A better understanding of neuroscience and most importantly neural control and sensory experience with machines can be the products of the convergence of brain-machine interface, nanotechnology, machine learning, and AI. Although the very promising combination of the neural prosthesis, nanoengineered BMI, and machine learning is yet to come, machine learning algorithms are taking a big part in nanotechnological and molecular research. Deep learning convolution neural networks find application in DNA sequencing experiments for analyzing and extracting single-molecule data.
Integration Of AI And Biotechnology
In bioinformatics, an emerging multidisciplinary field, integrating AI and biotechnology shows promising results. Application of available AI methods can be included in protein structure prediction, crucial for drug development and the understanding of biochemical effects. Reinforcement learning is a special AI technique, which can generate its own data only through integration with the environment.
This model can prove itself very powerful in certain functions, as it requires little training data. However, there is still a place for improvement for this method, adding several strategies, as its predictions are not conclusive yet. Artificial Intelligence and machine learning are put to use in DNA sequencing, predicting from huge amounts of data, characterizing proteins with protein’s reactant job and organic capacity, investigating the quality, recognizing certain areas of quality.
New Possibilities For Natural Sciences Because Of Inventions Such As Brain-Machine Interface
The combination of AI and biotechnology has many strong features among them creating new possibilities for natural sciences and speeding up the process of tedious laboratory tasks without tacit knowledge. Apart from the numerous advantages of the convergence of AI and biotechnology, there are some downsides to this matter.
Every technological advancement has risks associated with its approaches. Inadequate use of these technologies either through carelessness or deliberate harm can result in drastic consequences, even leading to a global catastrophic biological risk. The rising potential of such threats has an effect on health security demanding from it a deeper understanding and thorough examination.
The Risks Of Combining AI And Biotechnology
There are some frameworks assessing the risk of the combination of AI and biotechnology, nevertheless, they still need further improvement. Admittedly it is an extremely difficult task to evaluate the risks of novel technological solutions. In this case, the work is even more challenging because the combination of technologies adds complexity and multidisciplinary components.
There must be considered the possible outcomes of AI development as well as the outcomes of developments in natural sciences, which leads to the multiplication of uncertainties. Conclusive prediction of all risks can be impossible due to unreliability of technological outcomes, limited access to data, which results in problems with quantitative assessment of risks. Frameworks either focus solely on the good possible outcome or a complete failure. Such scenarios make it hard to capture the broad spectrum of events.
Synthetic Biology And Its Applications Apart From Brain-Machine Interface And Precision Medicine
Synthetic biology can be both applied in a beneficial way and used to engineer pathogens for nefarious purposes. AI and machine learning are great tools to help design, build and test in synthetic biology process. Although there is a technical barrier for pathogen creation, controversial studies have shown the possibility of synthesizing live viruses, like horsepox and the 1918 flu.
The goal of research techniques is to print viable gene-length sequences of DNA. Unfortunately, it is associated with a high risk of lowering technological barriers and widespread use. AI could probably open doors for dangerous actors by lowering entry barriers. Intruders would have the opportunity to design harmful pathogens.
Moreover, the information about genetic functions responsible for vulnerabilities and interconnections between the immune system and microbiome identified by deep learning algorithms could be used to engineer precision maladies to harm the immune system or microbiome. What is more leakage of information about the pathogenic potential of natural and synthetic DNA, may allow malicious use. Vulnerability in AI systems can pose a threat of cyber-attacks, which would allow intruders to disrupt laboratory design and biosecurity and grant access to confidential information.
Artificial Intelligence has greatly impacted the development of biotechnology, in a direct way contributing to many discoveries. Its functions will most likely become a crucial part of biotechnology, taking into consideration the amount of data and its complex interactions in this field. AI’s capacity to quickly access multivariate information is bringing biotechnology to a higher level of advancement. Nonetheless, biosecurity risks should be examined carefully with acknowledgment of the complexity of convergence of technologies.