Recently I came across a fantastic article that explored how far ahead Google Maps is compared to Apple Maps, focusing in particular on Areas of Interest (AOI), and how this is achieved with Googles competencies in massive data and massive computation, resulting in a moat. The conclusion is that Google has gathered so much data, […]
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Deep learning (DL) is all the rage these days and this approach to predictive modeling is being applied to a wide variety of problems, including many in computational drug discovery. As a dilettante in the area of deep learning, I’ve been following papers that have used DL for cheminformatics problems, and thought I’d mention a […]
I just got back from ACoP7, the yearly meeting of the International Society of Pharmacometrics (ISoP). Now, I don’t do any PK/PD modeling (hence the “strange land”) but was invited to talk about our high throughput screening platform for drug combinations. I also hoped to learn a little more about this field as well as […]
The topic of algorithmic fairness has started recieving a lot of attention due to the ability of predictive models to make decisions that might discriminate against certain classes of people. The reasons for this include biased training data, correlated descriptors, black box modeling methods or a combination of all three. Research into algorithmic fairness attempts […]
Update (07/28/16): DrugBank/OMx have updated the licensing conditions for DrugBank data in response to concerns raised earlier by various people and groups. See here for a detailed response from Craig Knox A few days back I came across, via my Twitter network, the news that DrugBank had changed their licensing policy to CC BY-SA-NC 4.0. As […]