Vinay Rathi

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On November 12, 2018, Vinay Rathi commented on Earth to everyone: we need your help! (Open innovation at NASA) :

Extremely well-written – would expect to read something of this caliber in print. I do wonder if the author might consider a more balanced perspective on the role of “traditional” scientific staff at NASA. Without understanding the particulars of anecdotal evidence cited above, I presume that these staff may be instrumental in identifying high-quality ideas amongst the crowd and realizing their technical potential. However, from what is written, it seems that promoting a more open-minded discovery culture would allow NASA to further develop these capabilities amongst internal talent.

A well-written and insightful piece. The author attributes Boeing’s success to the development of additively manufactured components. I think one implication of this argument worth clarifying is the apparent adeptness with which they have integrated these components into their production processes. I would be interested to learn more about their strategy in this regard; given technical similarities between aircraft and their relatively long duration of production, I wonder if and how Boeing is leveraging additive manufacturing to minimize work-in-process inventory.

On November 12, 2018, Vinay Rathi commented on BMW Bets on 3D Printing your Next Sports Car :

I enjoyed reading this piece. Would appreciate additional perspective from the author on a few questions listed below:

1. The author mentions venture capital investment by BMW in additive manufacturing start-ups. How does the author imagine BMW will leverage the potential success of these firms to improve internal capabilities and production?

2. It seems that additive manufacturing offers potential for automobile customization. Does the author have any insight into such potential applications of this technology?

3. How does the author anticipate that 3-printing will affect the market dynamics in the vintage automobile industry? I wonder if increased access to spare parts would result in price increases, as maintenance may pose less of a barrier to purchase.

A well-written and interesting piece. Would appreciate hearing the author’s perspective on a two questions listed below:

1. The wisdom in leveraging crowdsourcing to elicit the preferences of the crowd seems apparent. However, I wonder how Pepsi selects among the most “popular” ideas put forward. For instance, choosing the idea with the most votes may be problematic if the population participating in these initiatives is not representative of all consumers, limited in size, or both. Does the author have an insight as to how Pepsi protects against the downside risk of extrapolating on crowdsourced ideas such potentially risky situations?

2. The author argues that the initial investment in start-ups focused on potential products of tomorrow is quite low. However, subsequent investment decisions in the product development lifecycle may be more complicated and costly. Does the author have any insight as to how Pepsi would evaluate such decisions (e.g., through a stage-gating approach as performed by pharmaceutical companies)?

A very well-written piece, though a bit light on the scientific underpinnings of this novel approach to drug discovery. Would appreciate additional perspective from the author on the questions below:

1. The author does a nice job explaining how machine learning can be applied to accelerate understanding of chemical modifications to compounds. This likely presumes a worthwhile base compound to alter. Did the author discover any information on machine learning applications geared towards the discovery of novel compounds?

2. As noted by the author, this technology is yet unproven. The evidence cited in the piece refers to “candidate-quality” molecules. The strength of this evidence depends on the definition of “candidate-quality.” Does the author have insight into the odds of “candidate-quality” molecules materializing as marketable therapeutics?

3. The author mentions biologics in the form of vaccines (ie, preventative medicines). Did the author encounter any information on therapeutic biologics, such as those used to treat rheumatoid arthritis? I wonder if network models of disease states (rather than host organisms; e.g., inflammation in HLA-B27 autoimmune disorders) would be similarly applicable.

On November 12, 2018, Vinay Rathi commented on Machine Learning in Drug Discovery :

Hi Stacy,

I very much enjoyed your piece, which was well-written. A few points to consider:

1. The $2.6 billion figure you have cited is perhaps exaggerated. Would refer you to Jerry Avorn’s excellent Perspective in NEJM (“The $2.6 Billion Pill — Methodologic and Policy Considerations”, 2015) on the matter. Half the figure comes from a cost of capital estimated at 10.6%!

2. The data inputs mentioned vary widely in nature. I would be keen to hear more about which applications (e.g., designing new molecular entities versus identifying the most promising investigational compounds synthesized to date) may be most impactful in the near future.

3. Difficulties with synthesizing published data – which are often quite heterogenous in presentation and quality – aside, what are your thoughts on potential shortcomings of machine learning secondary to known publication bias?