인공지능 기술을 활용한 신약개발은 현재 국내외에서 활발하게 진행되고 있습니다. 인공지능을 활용하면 신약 개발 기간과 비용을 획기적으로 단축할 수 있기 때문입니다. 예를 들어, 인공지능 기계학습 알고리듬을 활용하면, 신약 후보 분자를 탐색하는 과정에서 수많은 빅데이터를 분석할 수 있어, 시간과 비용을 절감할 수 있습니다.

인공지능이 신약개발에 도움을 준다고는 하지만, 이는 인공지능을 제품에 활용할 제품개발은 여전히 천문학적인 비용이 수반됩니다. 비용을 들인 결과는 수십년 걸릴 개발기간을 당겨주는 정도에 해당하죠. 이런 상황에서는 제아무리 거대 제약사라도, 우선순위 산정에 고민을 하게 될 것입니다.

투여되는 자본 대비 이익률이 가장 큰 품목부터 개발하게 될 것입니다. 그러므로, 기술의 발전이 모든 의약품이 일시에 개발될 수는 없는 현상을 감안한다면, 기술 이외에도 넘어야 할 장애물이 여전히 많이 존재하는 것을 알게 됩니다.

There are several genes related to hair loss. For example, chr20p11 is one of the well-known genes that are highly associated with androgen alopecia. In addition, genes such as IL2RA, HLA-DQB1, and EDAR are also known to be related to hair loss.

However, it is still difficult to accurately predict the possibility of hair loss through genetic testing. This is because hair loss follows a multi-factor method.



Recently, various studies on hair loss treatment are being conducted in the overseas medical community. Various options, such as fundamental treatment through gene therapy and how to use various drugs, are being discussed.

Gene therapy is a way to solve the root cause of hair loss, which is currently being studied. In addition, new treatments for hair loss are being developed recently, and these treatments work in various ways depending on the cause of hair loss.

Minoxidil, for example, is one of the drugs used to prevent and treat hair loss. Minoxidil has the effect of inhibiting the action of hormones that cause hair loss and promoting hair growth. In addition, Finasteride is a drug used to treat male hair loss and is effective in preventing and treating hair loss by inhibiting the action of hormones that cause hair loss.

In addition, treatments using anti-inflammatory drugs and antioxidants are also being studied. However, research on hair loss treatment is still in its infancy, and more studies are needed to find a complete treatment.



New drug development using artificial intelligence technology is currently being actively carried out both at home and abroad. This is because using artificial intelligence can drastically shorten the duration and cost of new drug development. For example, using an artificial intelligence machine learning algorithm, it can analyze a lot of big data in the process of searching for new drug candidate molecules, saving time and money.



Although artificial intelligence is said to help develop new drugs, product development for which it will be used in a product still has astronomical costs. The cost-effective result is that it accelerates the development period that will take decades. In this situation, no matter how big a pharmaceutical company I am, I'm going to be thinking about prioritizing.



The items with the highest return on capital to be administered will be developed first. Therefore, if technological advances take into account the phenomenon that not all drugs can be developed at once, we find that there are still many obstacles to overcome other than technology.