godfather of surgery

Chapter 1256 Steady Progress
Outside, the winds were raging, but Yang Ping, who was in the eye of the storm, remained calm and composed, steadily moving forward.

Scientific research cannot be rushed; it can only proceed according to its objective laws. Sometimes, being too hasty can easily lead to mistakes.

His research doesn't start from the surface; he goes straight to the root of the problem. To bypass existing research tools, the core methodology must be restructured. Just like with transportation, when you can't surpass your competitors in internal combustion engine technology, you must restructure automotive powertrain technology—using electric motors. This is the basic logic of new energy vehicles. In the field of internal combustion engines, it's impossible to overturn the technological barriers built up over a century ago, but restructuring powertrain technology is different. This isn't about taking a shortcut; it's about substitution, choosing an alternative path.

Yang Ping's current goal is to replace existing research, to choose a new path, and to reconstruct research objectives, methods, and tools, rather than desperately chasing after others within their existing circles. The email he sent to the experimental team was like a few brilliant flares thrown into the fog, instantly pointing the way forward for the team members who were stuck in a stalemate. This path was completely new, a route they had never imagined before.

The emails were so detailed, the ideas so bizarre, and the feasibility so high that He Zijian, Wang Chao, and others could hardly believe their eyes when they read them carefully.

They had heard of Professor Yang before; he was said to be extremely innovative, with wildly imaginative ideas, an extremely precise grasp of research directions, and highly feasible solutions. Following his ideas in experiments, they rarely went astray. Now they had truly witnessed Professor Yang's incredible abilities, and the word "genius" had become a reality in their minds.

"Professor... how did you do that?" He Zijian stared at the brand-new AI protein design parameter set on the screen, muttering to himself. This parameter set completely transcended the framework of commercial software he was familiar with, as if it directly outlined the optimal balance between energy and function of protein molecules from a higher dimension. He tried substituting several previously stuck design sequences into this new parameter set, and the computing power provided by the Digital Medicine Center of Nandu Medical University immediately yielded exciting results—the designed virtual protein structure was more stable, and its predicted binding capacity to the target receptor was significantly improved.

This is what dimensional reduction attack is. Professor Yang's thinking is on a completely different level from existing technological paths.

What Wang Chao received was an improved blueprint for a miniaturized biosensor, which even included suggestions for probe molecule structures optimized for specific wavelengths of domestically produced lasers, as well as a set of signal processing algorithm core logic that could be described as "ingenious".

"Brilliant!" he exclaimed, slapping the table in admiration. "Using this digital lock-in amplification technology combined with wavelet transform noise reduction, theoretically we could really push the sensitivity of our 'clumsy' equipment to the picomolar level! Professor Yang even knows this?"

Wang Chao marveled at Professor Yang's breadth of knowledge, marveling at the sheer number of monographs and papers he had read to achieve such profound expertise. He not only possessed extremely deep and extensive knowledge of medical science, but also had a profound understanding of related tools and techniques.

Chu Xiaoxiao received a solution for AI image recognition to replace complex streaming panels, along with detailed model training data specifications. She immediately collaborated with algorithm engineers at the Digital Medicine Center to build the dataset and train a dedicated convolutional neural network model according to the specifications.

Based on the key control points of the enzyme process provided by Yang Ping, Liu Yang readjusted the fermentation and purification process, resulting in a visible improvement in efficiency.

The list of domestic alternative reagents and equipment and the performance evaluation standards in Jiang Jitong's hands have become the "procurement guide" for the entire project, greatly shortening the material and equipment search and verification cycle.

Yang Ping did not explain the origin of these plans, and the team members tacitly refrained from asking. In their minds, Professor Yang had already set a precedent for this, demonstrating an extremely precise grasp of scientific research that was truly astonishing; he simply possessed such talent.

Under immense pressure and with a shared goal, an almost fanatical creativity and fighting spirit were ignited and permeated the laboratory.

Despite Professor Yang's guidance, the real-world path to overcoming these challenges remains fraught with difficulties, because Professor Yang only provided a general idea. Although this idea was very specific, much of the subsequent work required them to complete it step by step.

Wang Chao's team's miniature biosensor was the first major challenge. According to Yang Ping's blueprint, they needed to integrate a domestically produced laser, a photomultiplier tube, and a self-made microfluidic chip into a palm-sized device. Optical alignment, fluid control, signal interference… every step presented numerous problems.

"Dr. Wang, the background noise is too high again!" a researcher said in frustration, staring at the wildly fluctuating baseline on the screen.

Wang Chao recalled Yang Ping's mention of "electromagnetic shielding and grounding strategy optimization" in the email, took a deep breath, and said: "Disassemble! Re-inspect all the interfaces, thicken the shielding layer, and run a separate ground wire. Algorithm team, get ready, we'll add the noise reduction algorithm Professor Yang gave us once the signal is more stable!"

Failure after failure, adjustment after adjustment. The corners of the lab were piled high with discarded chips and circuit boards. But under Yang Ping's almost prophetic guidance, they avoided many fatal detours. Finally, after dozens of failed attempts, the oscilloscope captured a weak but clear specific binding signal!
"It's out! Pimoore level! We did it!" Excited cheers erupted in the laboratory. This seemingly crude "cobbled-together" device had actually reached the core performance level required for equipment that was previously restricted from import. This was not only a technological breakthrough, but also a rebuilding of confidence.

Chu Xiaoxiao's journey in AI image recognition was equally bumpy. The initial model's accuracy in identifying cell subpopulations was abysmal. Images captured by domestically produced imaging systems had relatively low resolution and noticeable noise.

"The images taken by this machine are indistinguishable to the human eye, how can AI learn them?" a student complained.

Chu Xiaoxiao recalled a sentence from Yang Ping's document: "We don't need to copy the massive amounts of data from imported equipment, but rather teach AI to grasp the most critical features. In the data desert, the wisdom of algorithms is more important than the accumulation of data."

She led her team, along with algorithm engineers, to preprocess and enhance the images. Simultaneously, guided by Yang Ping's "key biomarkers," they re-annotated the training data, directing the AI ​​to focus on subtle morphological and fluorescence pattern changes that truly determine immune activation. After countless iterations of training, the CNN model began to exhibit astonishing "intuition." It could even accurately identify potentially valuable target cells from a seemingly chaotic array of signals, its screening efficiency and accuracy gradually approaching the levels previously achievable only with complex multicolor panels and high-end flow cytometers.

Therefore, changing the path is very important, but opening up a new path is easier said than done.

Liu Yang's self-developed enzyme system has also achieved a stage of success. Guided by the key control points pointed out by Yang Ping, they successfully expressed and purified a universal enzyme with qualified activity, at a cost of only one-tenth that of imported products. Although batch-to-batch stability still needs further improvement, this means that they have begun to get rid of the risk of being held back in terms of the most basic "ammunition" supply.

He Zijian's design work was further amplified. The new AI design module continuously produced optimized candidate molecular sequences, which were quickly handed over to Wang Chao's sensors for initial interaction verification, and then to Chu Xiaoxiao's AI model for cellular-level functional screening. A rudimentary but fully autonomous "design-verification-screening" internal loop was running steadily and diligently in the laboratory.

The progress in the real world, in turn, provided Yang Ping with the most valuable "practical" data for optimization in the system space.

After all, Yang Ping's experiments in the system space were very free, while the real world would encounter various objective limitations. Therefore, Yang Ping was not idle and made adjustments and optimizations based on the difficulties they encountered in reality.

Every night, Yang Ping would enter the system space and import the new problems and data that each group had encountered during the day. The real world was like a huge, unpredictable testing ground, constantly challenging and refining the model he had built in the system space laboratory.

What temperature drift problem did Wang Chao's sensor exhibit when used in real-world applications? In the system space, Yang Ping immediately simulated thousands of temperature compensation algorithms, found the optimal solution, and sent the updated code to Wang Chao early the next morning.

Did Chu Xiaoxiao's AI model misjudge a specific cell morphology? Yang Ping used supercomputing power in the system space to generate a massive number of simulated cell images with this specific morphology, and then used them to reinforce and fine-tune the model to improve its generalization ability.

Liu Yang's enzyme preparation experienced activity decay during scale-up production? The system instantly performed a full scan of process parameters from laboratory shake flasks to pilot-scale fermenters, identifying key bottlenecks and providing optimization solutions. This process was no longer a one-way output from Yang Ping, but rather a powerful closed loop of "reality feedback - system optimization - further guidance of reality." Yang Ping's role is more like the "brain" and "evolutionary engine" of this independent R&D ecosystem.

He was no longer satisfied with solving individual technical issues, but began to focus on connecting and optimizing the "meridians" of the entire platform.

In the system space, he began to build a unified data aggregation and intelligent scheduling hub. All data generated by all modules—AI design parameters, sensor binding curves, AI model recognition results, enzyme activity data—were required to be uploaded to this virtual hub in a standard format.

Then, he introduced more advanced reinforcement learning algorithms, so that this central AI would no longer passively receive data, but actively learn the inherent laws of the entire R&D process.

One evening, within the system space, Yang Ping set a brand new task: targeting a difficult-to-break immune target, the system's computer platform was required to autonomously design a candidate molecule with high activity and low toxicity within a limited time.

He initiated the entire process.

The AI ​​design engine generated the first batch of 100,000 virtual protein molecules based on the target structure.

These molecules underwent rapid initial screening, leaving one thousand of the most promising ones.

These thousand molecules were sent to a virtual expression and purification platform to simulate Liu Yang's process, assess their manufacturability, and eliminate a batch of molecules that were difficult to express.

The remaining molecules enter the virtual biosensor platform to simulate the binding strength with the target, eliminating another batch of molecules with weak binding forces.

The bound molecules then enter a virtual cell screening platform, where an enhanced version of Chu Xiaoxiao's AI model predicts their immune activation effects and potential cytotoxicity.

Ultimately, only a few dozen molecules made it to the "final round".

At this point, the reinforcement learning AI in the data hub began to play its role. It analyzed the common structural features, physicochemical properties, and performance data of these dozens of successful molecules in previous modules, summarizing a set of "successful patterns." Then, it fed these patterns back to the original design engine.

The second round of AI design is no longer a blind search starting from scratch, but a targeted optimization based on the "successful experience" of the first round! The newly generated molecules start from a higher level.

This cycle repeated itself hundreds of times at an astonishing rate within the system space. With each iteration, the central AI gained a deeper understanding of "how to design good molecules," and it even began to discover counterintuitive structure-activity relationships that human scientists struggle to detect through experience.

As dawn broke once more, Yang Ping exited the system space, holding in his mind three candidate molecular sequences that had undergone countless virtual iterations and optimizations, demonstrating exceptional performance across all simulation stages. These three molecules were not the product of his personal inspiration, but rather the culmination of this nascent, self-developed research and development ecosystem's "autonomous evolution" driven by AI!
At the morning meeting, Yang Ping sent the sequences and information of the three candidate molecules to the team.

"This is... designed independently by the platform?" He Zijian stared at the exquisite yet unconventional structure, utterly astonished. He asked himself, even with AI assistance, he might not have been able to come up with such a design.

“Yes.” Yang Ping nodded calmly. “Our platform has already initially acquired the ability to ‘learn’ and ‘evolve.’ Next, we need to synthesize them in reality and conduct comprehensive in vitro and in vivo verification.”

He assigned a new task:

He Zijian: Coordinate with the synthesis outsourcing company to synthesize these three candidate molecules as soon as possible.

Wang Chao and Chu Xiaoxiao: Prepare the verification process so that once the molecules are in hand, we can immediately conduct high-standard evaluations of their bioactivity and specificity.

Liu Yang: We have begun to study the development of large-scale production processes for these three molecules in preparation for future commercialization.

Jiang Jitong: Finding and evaluating compliant collaborative platforms that can conduct preclinical animal experiments.

After the meeting, Yang Ping stood alone by the office window. Below, the city pulsed strongly. His heart, too, surged with an unprecedented torrent of emotions.

He knew they were about to take the most crucial step. If these three candidate molecules were successfully validated in reality, it would not only be the dawn of a new adjuvant, but a paradigm victory. It would prove that a completely new path, entirely different from relying on Western scientific research infrastructure, was feasible, and potentially more efficient and innovative.

What he created was not merely a tool to solve the current predicament, but an innovative life form capable of continuous self-learning, self-evolution, and the nurturing of limitless possibilities. The value of this system has long surpassed that of immune adjuvants, and even transcended drug development itself. It is a methodology, a Chinese solution for achieving high-level technological self-reliance and strength in a "bottleneck" environment.

"The hardware supply chain, the software system ecosystem... the foundation has been laid." Yang Ping's gaze turned to the distance, where a larger chessboard seemed to be unfolding. "It's time to let this system meet more complex and severe challenges."

He decided to take a bigger gamble, the stakes no longer being the success or failure of a single product, but whether China could truly possess the ability and confidence to define the rules and lead the future in the cutting-edge field of biotechnology.

Now he firmly believes that it is entirely possible. The alliance formed by Ruixing has completed the initial talent aggregation. Given enough time, they will definitely be able to complete the research, development, improvement, and production of cutting-edge scientific research equipment for the laboratory and build their own scientific research platform.

This decisive battle has just reached its climax.

(End of this chapter)