WEBVTT - Quick Bite: Can AI Speed Up Drug Discovery? 0:00:00.560 --> 0:00:03.280 We'd like to interview you about how we can make 0:00:03.400 --> 0:00:06.600 shared lunch better. We've got a survey happening right now 0:00:06.680 --> 0:00:09.360 and we'd love your feedback. It just takes a few minutes, 0:00:09.560 --> 0:00:11.879 and if you're a New Zealand resident, you can go 0:00:11.920 --> 0:00:15.200 on the drawer to one of six fifty dollars Shares's gifts. 0:00:15.480 --> 0:00:17.280 The link is in our show notes. 0:00:17.440 --> 0:00:24.880 Right now you're listening to a Chasi's podcast, just getting 0:00:24.920 --> 0:00:30.760 back to manufacturing these pharmaceuticals. Has AI had much of 0:00:30.800 --> 0:00:33.720 a place here in terms of speeding things. 0:00:33.600 --> 0:00:37.279 Up, So this AI kind of in farmer companies and 0:00:37.280 --> 0:00:40.360 in drug discovery. It's been a topic that investors are 0:00:40.440 --> 0:00:44.239 kind of increasingly focused on and increasingly excited about. And 0:00:44.280 --> 0:00:47.239 one of the key kind of themes or takeaways I 0:00:47.280 --> 0:00:49.400 would say that I really noticed when I was in 0:00:49.440 --> 0:00:52.479 the US is just how excited companies are about the 0:00:52.600 --> 0:00:55.360 use of AI and drug discovery. Because we've already talked 0:00:55.400 --> 0:00:59.320 about how time consuming and expensive it is to develop 0:00:59.400 --> 0:01:02.720 new medicine, and what we've seen is that the developments 0:01:02.760 --> 0:01:07.600 in AI mean that you can develop new molecules around 0:01:07.760 --> 0:01:10.240 ten times as fast, or so you can design new 0:01:10.240 --> 0:01:13.520 molecules around ten times as fast. You can run simulations 0:01:13.520 --> 0:01:17.280 on those molecules around one hundred times as fast. And 0:01:17.319 --> 0:01:19.760 then there's this new concept, which I think is pretty interesting. 0:01:19.760 --> 0:01:21.759 You've heard of the concept of a self driving car. 0:01:22.080 --> 0:01:25.319 There's this new concept of a self driving lab. And 0:01:25.360 --> 0:01:28.640 so what this means is that you have AI and 0:01:28.680 --> 0:01:31.840 then you have robotics, and so you use AI and 0:01:31.959 --> 0:01:35.080 robotics together and they drive the cycle of kind of prediction, 0:01:35.280 --> 0:01:40.240 experimentation and analysis, which means that you can iteratively identify 0:01:40.319 --> 0:01:43.680 new compounds which you then run experiments on, which then 0:01:43.760 --> 0:01:46.200 mean that the whole process is much faster and then 0:01:46.240 --> 0:01:48.840 importantly more cost effective for companies. 0:01:49.240 --> 0:01:51.800 Gosh, So do we have any idea how much that 0:01:51.800 --> 0:01:54.520 would speed things up? We talked about ten years before 0:01:55.000 --> 0:01:58.280 being the time that it can take, I think for exclusivity, 0:01:58.320 --> 0:02:00.000 but I think you know, when you've got a meticine 0:02:00.560 --> 0:02:02.840 in the wings, that can take even longer, can't it. 0:02:03.040 --> 0:02:06.040 Yeah, And I think it's probably too early yet to 0:02:06.120 --> 0:02:09.240 put an actual number on how much it could speed 0:02:09.320 --> 0:02:11.400 things up. But I think what we're seeing is more 0:02:11.440 --> 0:02:13.639 and more companies are talking about it, more and more 0:02:13.639 --> 0:02:17.080 companies are using it in their drug discovery process. So 0:02:17.200 --> 0:02:19.880 I think over time we'll be able to get a 0:02:19.880 --> 0:02:23.640 better sense of how much time it actually is taking 0:02:23.680 --> 0:02:26.480 off the drug discovery process. But to the extent that 0:02:26.520 --> 0:02:30.760 it means we can develop medicines that are cheaper, I 0:02:30.760 --> 0:02:32.520 feel like that could only be a good thing given 0:02:32.560 --> 0:02:34.960 the high costs of healthcare that we're seeing around the 0:02:34.960 --> 0:02:35.959 world now. 0:02:36.000 --> 0:02:39.799 Just thinking about investors looking to invest in the sector, 0:02:40.440 --> 0:02:43.360 I mean, there are those direct stocks, they are probably 0:02:43.440 --> 0:02:46.320 quite expensive at the moment for some people. But also 0:02:46.520 --> 0:02:49.560 what about ETFs. I know on cheeseys, for instance, we've 0:02:49.639 --> 0:02:53.359 got about five or at least there's more actually pharmaceutical 0:02:53.400 --> 0:02:56.720 ETFs that people can look to. They are mainly in 0:02:56.760 --> 0:02:59.600 the US. But also I did see that there was 0:02:59.760 --> 0:03:05.640 a couple of applications for more themed weight loss ETFs 0:03:05.680 --> 0:03:09.440 by I think Amplify and round til investments. We're two 0:03:09.560 --> 0:03:12.519 though I don't think they've actually come to fruition yet. 0:03:12.800 --> 0:03:15.720 So I guess when you're thinking about healthcare, healthcare itself 0:03:15.880 --> 0:03:19.760 is super broad, and then within that there's all different 0:03:19.840 --> 0:03:24.640 kinds of companies, so hospitals, medical device companies, companies that 0:03:24.720 --> 0:03:27.400 sell chemicals that are used in farmer companies that sell 0:03:27.480 --> 0:03:29.960 tools that are used in farmer, so all of that 0:03:30.080 --> 0:03:32.600 kind of sets in the broader healthcare space. And I 0:03:32.639 --> 0:03:35.200 think if you're thinking about getting a broad kind of 0:03:35.240 --> 0:03:38.600 ETF exposure, maybe one way to get a sense of 0:03:39.120 --> 0:03:41.720 what could be better is looking back at what's happened 0:03:41.720 --> 0:03:44.520 over time. And if you look back over a significant 0:03:44.520 --> 0:03:47.160 period of time, say twenty years, the S and P 0:03:47.400 --> 0:03:50.800 five hundred Index and the S and P Healthcare Index 0:03:51.080 --> 0:03:54.720 have actually interestingly performed basically neck on neck, so they 0:03:54.760 --> 0:03:57.760 both generated around a ten percent return per year over 0:03:57.800 --> 0:04:01.960 a twenty year time horizon. If you look at farmer itself, 0:04:02.280 --> 0:04:06.560 that generated around half a percent lower return, so around 0:04:06.680 --> 0:04:09.800 nine and a half percent over that twenty year period 0:04:09.800 --> 0:04:12.840 of time. But if you had managed to pick farmer 0:04:12.880 --> 0:04:17.359 winner Novo or farmer winner Eli Lilly over that period 0:04:17.400 --> 0:04:20.440 of time, you would have generated I think from memory 0:04:20.480 --> 0:04:23.200 it's around a sixteen percent return per ANIM and then 0:04:23.240 --> 0:04:25.839 a twenty five percent return per ANIM. Picking a winner 0:04:26.240 --> 0:04:28.160 can be great. But then if you're looking at a 0:04:28.200 --> 0:04:32.119 diversified exposure, as she would suggest that rather than looking 0:04:32.200 --> 0:04:36.920 for a narrow farmer exposure, a broader health care exposure 0:04:36.960 --> 0:04:40.000 would be better. And then I think especially worth keeping 0:04:40.040 --> 0:04:45.159 in mind. With a diversified easier for Farmer, You're likely 0:04:45.240 --> 0:04:48.400 to have a higher exposure to the largest companies and 0:04:48.480 --> 0:04:52.000 they may have a higher proportion of medicines that are 0:04:52.000 --> 0:04:55.400 coming up to that loss of exclusivity and revenue and 0:04:55.440 --> 0:04:57.680 revenue dropping off. So that's a really important thing to 0:04:57.760 --> 0:05:01.040 keep in mind with Farmer itself, is what is that 0:05:01.080 --> 0:05:04.800 loss of exclusivity burden and how does that sit across 0:05:04.839 --> 0:05:06.640 the companies that are in that ETA. 0:05:06.760 --> 0:05:10.480 Investing involves risk you might lose the money you start with. 0:05:10.960 --> 0:05:14.760 We recommend talking to a licensed financial advisor. We also 0:05:14.800 --> 0:05:18.679 recommend reading product disclosure documents before deciding to invest.