The recent uproar over the birth of genetically modified baby twins in Chinahas given the world reason to pause. Dr. Jiankui’s “pandora’s box” has led to fresh bio-ethics debates everywhere. Unfortunately, the backlash will likely result in increased scrutiny of medical research as well as the biopharma industry at large. It has happened before.
The #crisprbabies controversy is just a reminder that healthcare, whether in the form of fringe science or even legitimate medicine, presents many risks- to patients, to doctors, to investors. Everyone.
For patients, no procedure or pill is completely safe. There are everyday reminders of this. A “routine” surgery requires a signature — a recognition that death is a possible outcome. Prescribed drugs may not actually work, and they could even actually cause harm. Who among us at this point has not seen the disclaimers of side effects like death or feelings of suicide.
Undaunted by these challenges, the industry has looked to novel places to mitigate risk. In drug discovery, the goal is to develop safe and effective drugs, cheaper and faster.
One area of exploration has focused on high performance computing (HPC). The idea here is to do as much as possible with computing, from identifying and designing “seed” molecules to target disease, all the way to predicting outcomes of clinical trials.
Efforts in computational drug discovery started in the seventies, and that train hasn’t since stopped. There have been plenty of skeptics, and they often point to the massive investments in this area, with little results to show.
The computational drug discovery hype engine alive and kicking in 1981.
The latest disruptive tech is, of course, AI, and everyone is trying to figure out how it will impact their industry, including biopharma. AI has made many promises in the past, but failed to deliver. The consequences were at times dire for the entire field of AI.
No. That's not AI.
That said, this latest wave of AI ( the fourth one since the 50s ) is in many ways different than its predecessors. It deserves the benefit of our collective doubt. This is because today’s AI shows up at an unprecedented time in our digital history:
There is a renewed sense of exploration in biopharma, and AI finds itself at the epicenter. AI is reenergizing not just interest in computational drug discovery but also massive investment- both in big pharma and startups.
An active conversation has started to take place, and many of these conversations are happening at the intersection of technology and science.
For example, a few weeks ago, I attended the AI Applications Summit in BioPharma conference at Harvard Medical School. It was a intimate gathering of about 120 executives and researchers and practitioners in biopharma, from universities as well as industry.
Some of the highlights included:
This summit was a 2 day event and it was packed with excellent content. I don’t work in biotech (I work in hardware-accelerated data science ), but I was able to follow most of the presentations. The organizers have managed to get in one venue many of the leading thinkers and implementors in biopharma AI. Everyone is very approachable and there are plenty of networking and breakout events where you can have one-on-one conversations.
One of the topics discussed at the conference echoes conversations in other AI circles, such as in vehicle autonomy and automated cybersecurity. And that issue is AI trust. Two areas of concern are AI bias and AI transparency. Can we trust the decisions that an AI agent makes ? Can we trust “black box” algorithms ?
Do you trust your AI with your life ?
I think the issue of AI bias comes down data collection and data curation. Data-driven techniques like machine learning are quite good at reflecting the data that feeds it. It’s often said of bad machine learning models — “garbage in, garbage out.” Where fairness is concerned, it’s “bias in, bias out.” A stunning example of this is Amazon’s AI-based recruiting tool that demonstrated bias against female candidates.
AI doesn’t tell you why.
Now, assuming one could level the playing field in data, we still have a problem. And that’s AI’s lack of transparency. A machine learning algorithm does not tell you how it came to its decision. Why was the candidate rejected ? Why was the loan not approved ? Why is the cell not cancerous ? The model does not say. And if you go about looking under the hood, all you’ll see are millions of weights and parameters, a mess of floating point numbers.
The stakeholders in biopharma AI will need to address these issues. If they go unresolved too long, big pharma may decide that AI adds additional risk to drug discovery instead of mitigates risk.
In light of these issues and doubts, the innovators believe AI will become the most important computational tool in biopharma. Will AI help discover better, cheaper, and safer medicines ? Some say it’s not a question of “if,” but “when.”
But it’s not likely to bomb-drop on us — a big-bang-like, Kurzweillean “singularity” event in which algorithms suddenly output the blueprints for super drugs. Even the most optimistic AI futurists are no longer so naive. The AI revolution in biopharma will be a sequence of small wins over the course many years, with a few setbacks and lessons learned along the way.
Numerate is taming the highly combinatorial design space of small molecule drug discovery.