When the local manufacturers break through

Strictly speaking, the domestic market is still in the early stage of development, so many different forms of products and multiple landing scenes, where to find a breakthrough? Three basic requirements must be met: first, a full understanding of the requirements and results of automation from the goal; Secondly, the technology can be realized, and the process is relatively fixed, meeting the premise of landing; Finally, the system/product is mature and reliable, and customers pay for it, forming a commercial closed loop.

Result-oriented: replicating experimental results and exploring new mechanisms

Results-oriented, we first need to know where the demand for laboratory automation is reflected. If you refer to industrial automation, the key is to reduce costs and improve efficiency, and the final result is a processed or assembled product.

In contrast, laboratory automation ultimately produces results that can be divided into two categories: first, the completion of repetitive actions to produce an accurate data, which is mainly for detection laboratories; The second is to obtain data through a designed experimental process, explore and understand new mechanisms, or screen target compounds, which is mainly for R&D laboratories.

The operation steps and processes involved in the above two processes are mainly for the repetition of the experiment. At present, the application of the detection scenario is relatively mature, and the research and development scenario often needs to use the experimental data to analyze the experimental steps and the specific details of the experiment, so as to optimize the experiment. From this point of view, laboratory automation is not only the operation of the action execution level, but also includes the sample flow, information flow and operation flow. In addition to the operation of the experimental instrument, it is also necessary to integrate the results of the experimental instrument or the data results of the experimental process.

The realization of the above functions generally needs to meet four requirements: AI engines, mobile platforms, multi-modal sensors, etc., to achieve a full range of data collection in the laboratory; Access third-party instruments through the central control system to optimize the working mode; The data of the whole process of the experiment were collected by visual sensors, mechanical sensors, etc., and the experimental process was continuously optimized by steps that were not normally observed or analyzed. AGV robots are introduced to ensure flexibility and connect physically more segmented "independent functional areas".

Landing premise: technology can be realized, to process

For the automated exploration of testing laboratories, its technical threshold is slightly lower, the current industry has relatively large-scale or mature applications, mainly for independent testing, clinical diagnosis of standard projects, and nucleic acid testing due to the epidemic "multiplied ten times", they generally have a large market size, but most of these areas have become competitive Red Sea.

For the exploration of research and development laboratories, entrepreneurs generally reflect that in fact, the above technology is not difficult to separate, difficult to integrate it, achieve full automation, and well cope with the "complexity" and "non-standard" of life science scenes - these are high flexibility and precision requirements, as well as the rise of demand in some emerging technology fields.

Typical new technologies include gene editing technology, IPS cell culture technology, etc., which will bring great changes to human health and life. In this process, automation and intelligence will greatly promote the development of the industry. Taking the gene sequencing industry as an example, automated sequencing technology throughput is now more than 10 million times higher than it was 20 years ago, and its development rate is far faster than Moore's Law in the semiconductor industry. Similarly, the premise of scale application in some emerging fields is also that the automation technology involved in many links has been broken through, so that things that were very difficult to do in the past can now be solved by processes and standardized means.

For example, Alphafold2's cracking of the protein molecular folding problem for amino acid sequence prediction, the progress of AI in crystal type prediction, and the scale application of mRNA therapy technology have all turned biological problems into computational problems. As well as the evolution of semiconductor technology, the computing power breakthrough has opened up the path of computing power to the research and development of new biological drugs. If these scattered technologies can be gradually connected, the entire mass trial and error behavior of biomedicine can be turned into repeated work by machines.

Taking the automated development of macromolecular drugs as an example, at present, deep learning has a significant effect on the improvement of protein structure prediction, which can accurately predict the rough shape of most proteins in three-dimensional space, and help biotechnologists identify and produce proteins, so as to make the research and development of innovative macromolecular drugs predictable and programmable, and improve the efficiency of the whole process of drug research and development. However, most of the subdivision directions are still in the early stage of exploration, and there are fewer actual landing cases.

Commercialization: Find buyers and form benchmarking cases

After the application of technology, finding benchmark customers is the last step to open up the closed loop of business.

Ideal is very plump, reality is very bony. Due to the high cost of overall laboratory automation construction (dominated by imports), customers with both rigid demand and payment ability are mainly concentrated in large CRO, CDMO companies, pharmaceutical companies, laboratory departments and third-party medical laboratories of large hospitals, as well as some government-funded benchmarked laboratory projects. But these customers generally lack interest in local brands.

A biological drug CDMO company innovative biological drug research and development and pharmaceutical center IT director told 36kR, "At present, because of the domestic robot equipment dead corner design, in addition to domestic alternative and much cheaper, can not find other reasons to use, not to mention the big factory is not poor money." However, he also said, "If it does not involve the core GMP part (some equipment with high precision requirements), it should be localized, and it will slowly penetrate."

It is said that in some non-high-end technology fields, Chinese brands already have significant advantages, such as automated pipette workstations, biological sample storage, detection probes, etc., which are not only much cheaper, but also easy to use; However, in some high-end automation equipment involving biology, computers, medicine, machinery industry and other fields of patents and technology, there are some upstream parts have not yet achieved localization, domestic brands are in a "stuck neck" state, the future in the supply chain or face challenges, will become an important card point for domestic brands to break through.

In addition, a necessary condition for the production line to move from the research and development stage to the laboratory application is that the production line is standardized and replicable. However, in the field of life science laboratory automation, customers are small, scattered and immature, "non-standard, small batch, multi-variety", "can not raise demand" has become a "common disease" in the industry. A startup company said that a customer had bluntly said: "I don't know what my needs are, your team will first stay in the field, talk to all departments and all links of people, to help us transform it."

To this end, local laboratory automation manufacturers often have to play the role of customer program planning and industry leaders, and need to polish products through benchmarking customers/cases to prepare for subsequent large-scale applications. A typical Youmeijia technology built its own Kunpeng laboratory and gradually accumulated benchmark customers through exploring cutting-edge research in life science. When asked why they want to build their own laboratory, MGI Technology CEO Huang Yuqing said, "to build such a laboratory investment is very big, the operation is very difficult, and the cycle of looking for customer cooperation is too long, it is better to do it themselves."

Huixiang Technology is cooperating with AI pharmaceutical company Insi Intelligence to build an intelligent robot drug research and development laboratory to explore fully automated applications in unmanned scenarios. It is reported that six companies of the same type came to bid for the order, and peers evaluated, "Even if you have to accept the order at a loss, the investment in the early stage is worth it."

And for what is really a good product, we also have a basic consensus: that is, the production line can make money, the model can be copied, "others can't do it" (IP), the subsequent cost is reduced, the deployment cycle is shorter, and it can be expanded to other types of customers, changing the market pattern. In this regard, a typical case is the Falcon series, a small flexible intelligent delivery system developed by Novo.

Is it worth it?

Early commercialization is difficult for every entrepreneur to face the problem, investors are also betting on a "life science laboratory intelligent" future. And when will it come?

The next 5-10 years will be concentrated

Liu Wei, managing partner of Zhang Ke Lingyi Venture Capital, said that the CRO industry can be likened to the development of this industry will be divided into three cycles: the first wave, the industry from scratch, "there are companies dedicated to do this thing", as in recent years, Xuanjian Technology, Benyao Technology and other companies focused on robot intelligent products have emerged; In the second wave, a group of companies began to be willing to pay, constantly run in trial and error, some companies to develop and grow, the market began to reshuffle; Finally, find out the demands of the first batch of companies, solve the universal problem, and then provide standard products, rapid replication.

"The first wave has just begun, and the opportunity is the biggest, and the next five to 10 years will be the stage of acceleration and even concentrated outbreak, when the market will have universal, cost-effective and independent intellectual property products." Moreover, for investors, the valuation of the upstream of the biomedical industry is also relatively reasonable, and it is an investment theme given by The Times.

According to the actual business attributes of the future company, Yang Xiaolong, partner of Innovation Works, pointed out that several types of industries can be evaluated: first, if it can become a labor-based company, the valuation can be compared to CRO companies such as Wuxi Apptec; Second, it can produce biological results, such as specific drugs or compounds, which have corresponding asset value, and can be valued for standard drug enterprises, generally using discounted cash flow method; The third is automation equipment, which can be used to value overseas listed companies such as Beckman, PE and Agilent.

However, he also stressed that given that this is a highly interdisciplinary field, in fact, it can also be valued with a comprehensive reference to the above three industries, which can produce a certain advantage premium and match the development potential of the industry.

The look of a "good target"

In the investment market, what is a good target? Yang Xiaolong believes that it is either a system driven by super key components, or it is to train it into a "brain" - that is, a system of human judgment, which can be boiled down to three elements: a "card neck" point, with independent intellectual property rights, and a special structure.

From the perspective of the industry chain, like other industries, downstream customers generally pursue cost-effective and "user-centered" solutions, and multinational manufacturers are generally difficult to meet the needs, which leaves opportunities for local manufacturers. However, the disadvantage of this model is that it is difficult to modularize and standardize - this is the biggest gap between niche market and blue ocean market, and the market valuation is often lower than standard products. For automation developers, the key is to strike a balance: the protocol developed for a specific project is gradually optimized into a standard module, through the assembly of standardized modules to meet customized needs, and fundamentally reduce research and development costs.

As for the landing form, when asked whether to choose software or hardware, most entrepreneurs, investors and even customers will vote for hardware or software and hardware integration.

Drawing on the successful overseas experience, Dassault BIOVIA (small molecule medicine) and Benchling (large molecule medicine), as the representative companies, entered the laboratory data recording market through self-developed software, the latter has been valued at 6 billion US dollars, and it can also form a business barrier through the asset-light software system. However, people in the industry generally believe that infrastructure is the entrance of life application science, and equipment is the carrier of data formation; Software tools alone cannot expand the market, especially in the domestic market.

One investor told 36kr that benchling was valuable because it began as a differentiated digital tool and gradually expanded into a communication tool for hundreds of thousands of professionals in the scientific community and industry, which deposited more and more templates and databases, "This is very valuable." However, in China, doing such tools will "roll" badly, not only to fight price, fight service, fight customer sentiment, Party A is also used to customization, most manufacturers may not go to PLG (Product-Led Growth, product-driven growth) "hang up".

The apparent barrier

The barriers of this format are mainly reflected in technology and team, customer reputation and industry awareness and accumulation.

First of all, laboratory automation is also regarded as a life science support industry, involving biology, chemistry, materials, electronics and machinery and other professional fields, the products of various subdivisions of the industry often need to be applied in multiple disciplines, usually requires a composite team for years of technology accumulation to develop and manufacture mature products.

Secondly, there is customer choice inertia. In general, downstream customers are very cautious about the choice of products, tend to purchase high brand recognition, good market reputation tool products, once locked suppliers are difficult to replace.

Finally, in the long run, in addition to "technical barriers", the real stable moat also comes from industry cognition, cost advantage and product matrix, which requires enterprises to take root in the scene, constantly accumulate data and polish products, and quickly start volume, and then have the opportunity to achieve cost advantages; In addition, in order to resist the cyclical risks of downstream customers, the company should also choose a good track and constantly improve the product matrix.

As China joins ICH, we need to look at the industry from a global perspective, and "Why China" has become an important topic - in the global market with a higher degree of marketization, the fields that give full play to the advantages of China's intelligent infrastructure will rise at a faster pace, including laboratory automation. Under the cultivation of the global market, future laboratory intelligence will also become a reality sooner.